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arviz.InferenceData.rename_vars — ArviZ dev documentation

InferenceData.rename_vars(name_dict=None, groups=None, filter_groups=None, inplace=False)[source]#

Perform xarray renaming of variable or coordinate names on all groups.

Loops groups to perform Dataset.rename_vars(name_dict) for every key in name_dict if key is a variable or coordinate names of the dataset. The renaming is performed on all relevant groups (like posterior, prior, sample stats) while non relevant groups like observed data are omitted. See xarray.Dataset.rename_vars()

Parameters:

name_dictdict

Dictionary whose keys are current variable or coordinate names and whose values are the desired names.

groupsstr or list of str, optional

Groups where the selection is to be applied. Can either be group names or metagroup names.

filter_groups{None, “like”, “regex”}, optional

If None (default), interpret groups as the real group or metagroup names. If “like”, interpret groups as substrings of the real group or metagroup names. If “regex”, interpret groups as regular expressions on the real group or metagroup names. A la pandas.filter.

inplacebool, optional

If True, modify the InferenceData object inplace, otherwise, return the modified copy.

Returns:

InferenceData

A new InferenceData object with renamed variables including coordinates by default. When inplace==True perform renaming in-place and return None

See also

xarray.Dataset.rename_vars

Returns a new object with renamed variables including coordinates.

rename

Perform xarray renaming of variable and dimensions on all groups of an InferenceData object.

rename_dims

Perform xarray renaming of dimensions on all groups of InferenceData object.

Examples

Use rename_vars to renaming of variable and coordinates on all groups of the InferenceData object. We first check the data variables of original object:

import arviz as az
idata = az.load_arviz_data("rugby")
idata

• posterior

  <xarray.Dataset> Size: 452kB
  Dimensions:    (chain: 4, draw: 500, team: 6)
  Coordinates:
    * chain      (chain) int64 32B 0 1 2 3
    * draw       (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team       (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      home       (chain, draw) float64 16kB ...
      intercept  (chain, draw) float64 16kB ...
      atts_star  (chain, draw, team) float64 96kB ...
      defs_star  (chain, draw, team) float64 96kB ...
      sd_att     (chain, draw) float64 16kB ...
      sd_def     (chain, draw) float64 16kB ...
      atts       (chain, draw, team) float64 96kB ...
      defs       (chain, draw, team) float64 96kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:23.841916
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9
      sampling_time:              8.503105401992798
      tuning_steps:               1000

  • Dimensions:
    • chain: 4
    • draw: 500
    • team: 6
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • team

      (team)

      <U8

      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype='<U8')

  • Data variables: (8)

    • home

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • intercept

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • atts_star

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

    • defs_star

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

    • sd_att

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • sd_def

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • atts

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

    • defs

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (6)

    created_at :

    2024-03-06T20:46:23.841916

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

    sampling_time :

    8.503105401992798

    tuning_steps :

    1000

  
  
• posterior_predictive

  <xarray.Dataset> Size: 2MB
  Dimensions:      (chain: 4, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 32B 0 1 2 3
    * draw         (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
    * match        (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
      home_team    (match) <U8 2kB ...
      away_team    (match) <U8 2kB ...
  Data variables:
      home_points  (chain, draw, match) int64 960kB ...
      away_points  (chain, draw, match) int64 960kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:25.689246
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
    • chain: 4
    • draw: 500
    • match: 60
  • Coordinates: (5)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • match

      (match)

      <U16

      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype='<U16')

    • home_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

    • away_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

  • Data variables: (2)

    • home_points

      (chain, draw, match)

      int64

      ...

      [120000 values with dtype=int64]

    • away_points

      (chain, draw, match)

      int64

      ...

      [120000 values with dtype=int64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:25.689246

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• log_likelihood

  <xarray.Dataset> Size: 2MB
  Dimensions:      (chain: 4, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 32B 0 1 2 3
    * draw         (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
    * match        (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
      home_team    (match) <U8 2kB ...
      away_team    (match) <U8 2kB ...
  Data variables:
      home_points  (chain, draw, match) float64 960kB ...
      away_points  (chain, draw, match) float64 960kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:24.120642
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
    • chain: 4
    • draw: 500
    • match: 60
  • Coordinates: (5)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • match

      (match)

      <U16

      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype='<U16')

    • home_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

    • away_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

  • Data variables: (2)

    • home_points

      (chain, draw, match)

      float64

      ...

      [120000 values with dtype=float64]

    • away_points

      (chain, draw, match)

      float64

      ...

      [120000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:24.120642

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• log_prior

  <xarray.Dataset> Size: 260kB
  Dimensions:    (chain: 4, draw: 500, team: 6)
  Coordinates:
    * chain      (chain) int64 32B 0 1 2 3
    * draw       (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team       (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      home       (chain, draw) float64 16kB ...
      sd_att     (chain, draw) float64 16kB ...
      sd_def     (chain, draw) float64 16kB ...
      intercept  (chain, draw) float64 16kB ...
      atts_star  (chain, draw, team) float64 96kB ...
      defs_star  (chain, draw, team) float64 96kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:24.377610
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
    • chain: 4
    • draw: 500
    • team: 6
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • team

      (team)

      <U8

      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype='<U8')

  • Data variables: (6)

    • home

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • sd_att

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • sd_def

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • intercept

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • atts_star

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

    • defs_star

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:24.377610

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• sample_stats

  <xarray.Dataset> Size: 248kB
  Dimensions:                (chain: 4, draw: 500)
  Coordinates:
    * chain                  (chain) int64 32B 0 1 2 3
    * draw                   (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499
  Data variables: (12/17)
      max_energy_error       (chain, draw) float64 16kB ...
      index_in_trajectory    (chain, draw) int64 16kB ...
      smallest_eigval        (chain, draw) float64 16kB ...
      perf_counter_start     (chain, draw) float64 16kB ...
      largest_eigval         (chain, draw) float64 16kB ...
      step_size              (chain, draw) float64 16kB ...
      ...                     ...
      reached_max_treedepth  (chain, draw) bool 2kB ...
      perf_counter_diff      (chain, draw) float64 16kB ...
      tree_depth             (chain, draw) int64 16kB ...
      process_time_diff      (chain, draw) float64 16kB ...
      step_size_bar          (chain, draw) float64 16kB ...
      energy                 (chain, draw) float64 16kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:23.854033
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9
      sampling_time:              8.503105401992798
      tuning_steps:               1000

  • Dimensions:
  • Coordinates: (2)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

  • Data variables: (17)

    • max_energy_error

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • index_in_trajectory

      (chain, draw)

      int64

      ...

      [2000 values with dtype=int64]

    • smallest_eigval

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • perf_counter_start

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • largest_eigval

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • step_size

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • n_steps

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • lp

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • diverging

      (chain, draw)

      bool

      ...

      [2000 values with dtype=bool]

    • energy_error

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • acceptance_rate

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • reached_max_treedepth

      (chain, draw)

      bool

      ...

      [2000 values with dtype=bool]

    • perf_counter_diff

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • tree_depth

      (chain, draw)

      int64

      ...

      [2000 values with dtype=int64]

    • process_time_diff

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • step_size_bar

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • energy

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

  • Indexes: (2)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

  • Attributes: (6)

    created_at :

    2024-03-06T20:46:23.854033

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

    sampling_time :

    8.503105401992798

    tuning_steps :

    1000

  
  
• prior

  <xarray.Dataset> Size: 116kB
  Dimensions:    (chain: 1, draw: 500, team: 6)
  Coordinates:
    * chain      (chain) int64 8B 0
    * draw       (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team       (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      atts_star  (chain, draw, team) float64 24kB ...
      sd_att     (chain, draw) float64 4kB ...
      atts       (chain, draw, team) float64 24kB ...
      sd_def     (chain, draw) float64 4kB ...
      defs       (chain, draw, team) float64 24kB ...
      intercept  (chain, draw) float64 4kB ...
      home       (chain, draw) float64 4kB ...
      defs_star  (chain, draw, team) float64 24kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:09.475945
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
    • chain: 1
    • draw: 500
    • team: 6
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • team

      (team)

      <U8

      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype='<U8')

  • Data variables: (8)

    • atts_star

      (chain, draw, team)

      float64

      ...

      [3000 values with dtype=float64]

    • sd_att

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

    • atts

      (chain, draw, team)

      float64

      ...

      [3000 values with dtype=float64]

    • sd_def

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

    • defs

      (chain, draw, team)

      float64

      ...

      [3000 values with dtype=float64]

    • intercept

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

    • home

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

    • defs_star

      (chain, draw, team)

      float64

      ...

      [3000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:09.475945

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• prior_predictive

  <xarray.Dataset> Size: 492kB
  Dimensions:      (chain: 1, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 8B 0
    * draw         (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
    * match        (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
      home_team    (match) <U8 2kB ...
      away_team    (match) <U8 2kB ...
  Data variables:
      away_points  (chain, draw, match) int64 240kB ...
      home_points  (chain, draw, match) int64 240kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:09.479330
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
    • chain: 1
    • draw: 500
    • match: 60
  • Coordinates: (5)

    • chain

      (chain)

      int64

      0

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • match

      (match)

      <U16

      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype='<U16')

    • home_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

    • away_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

  • Data variables: (2)

    • away_points

      (chain, draw, match)

      int64

      ...

      [30000 values with dtype=int64]

    • home_points

      (chain, draw, match)

      int64

      ...

      [30000 values with dtype=int64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:09.479330

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• observed_data

  <xarray.Dataset> Size: 9kB
  Dimensions:      (match: 60)
  Coordinates:
    * match        (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
      home_team    (match) <U8 2kB ...
      away_team    (match) <U8 2kB ...
  Data variables:
      home_points  (match) int64 480B ...
      away_points  (match) int64 480B ...
  Attributes:
      created_at:                 2024-03-06T20:46:09.480812
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
  • Coordinates: (3)

    • match

      (match)

      <U16

      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype='<U16')

    • home_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

    • away_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

  • Data variables: (2)

    • home_points

      (match)

      int64

      ...

      [60 values with dtype=int64]

    • away_points

      (match)

      int64

      ...

      [60 values with dtype=int64]

  • Indexes: (1)

    • PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:09.480812

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• unconstrained_posterior

  <xarray.Dataset> Size: 36kB
  Dimensions:  (chain: 4, draw: 500)
  Coordinates:
    * chain    (chain) int64 32B 0 1 2 3
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
  Data variables:
      sd_att   (chain, draw) float64 16kB ...
      sd_def   (chain, draw) float64 16kB ...
  Attributes:
      sd_att:   pymc.logprob.transforms.LogTransform
      sd_def:   pymc.logprob.transforms.LogTransform

  • Dimensions:
  • Coordinates: (2)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

  • Data variables: (2)

    • sd_att

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • sd_def

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

  • Indexes: (2)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

  • Attributes: (2)

    sd_att :

    pymc.logprob.transforms.LogTransform

    sd_def :

    pymc.logprob.transforms.LogTransform

  
  

In order to rename the data variables, we use:

idata.rename_vars({"home": "home_new"}, inplace=True)
idata

• posterior

  <xarray.Dataset> Size: 452kB
  Dimensions:    (chain: 4, draw: 500, team: 6)
  Coordinates:
    * chain      (chain) int64 32B 0 1 2 3
    * draw       (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team       (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      home_new   (chain, draw) float64 16kB ...
      intercept  (chain, draw) float64 16kB ...
      atts_star  (chain, draw, team) float64 96kB ...
      defs_star  (chain, draw, team) float64 96kB ...
      sd_att     (chain, draw) float64 16kB ...
      sd_def     (chain, draw) float64 16kB ...
      atts       (chain, draw, team) float64 96kB ...
      defs       (chain, draw, team) float64 96kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:23.841916
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9
      sampling_time:              8.503105401992798
      tuning_steps:               1000

  • Dimensions:
    • chain: 4
    • draw: 500
    • team: 6
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • team

      (team)

      <U8

      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype='<U8')

  • Data variables: (8)

    • home_new

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • intercept

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • atts_star

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

    • defs_star

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

    • sd_att

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • sd_def

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • atts

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

    • defs

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (6)

    created_at :

    2024-03-06T20:46:23.841916

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

    sampling_time :

    8.503105401992798

    tuning_steps :

    1000

  
  
• posterior_predictive

  <xarray.Dataset> Size: 2MB
  Dimensions:      (chain: 4, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 32B 0 1 2 3
    * draw         (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
    * match        (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
      home_team    (match) <U8 2kB ...
      away_team    (match) <U8 2kB ...
  Data variables:
      home_points  (chain, draw, match) int64 960kB ...
      away_points  (chain, draw, match) int64 960kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:25.689246
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
    • chain: 4
    • draw: 500
    • match: 60
  • Coordinates: (5)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • match

      (match)

      <U16

      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype='<U16')

    • home_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

    • away_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

  • Data variables: (2)

    • home_points

      (chain, draw, match)

      int64

      ...

      [120000 values with dtype=int64]

    • away_points

      (chain, draw, match)

      int64

      ...

      [120000 values with dtype=int64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:25.689246

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• log_likelihood

  <xarray.Dataset> Size: 2MB
  Dimensions:      (chain: 4, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 32B 0 1 2 3
    * draw         (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
    * match        (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
      home_team    (match) <U8 2kB ...
      away_team    (match) <U8 2kB ...
  Data variables:
      home_points  (chain, draw, match) float64 960kB ...
      away_points  (chain, draw, match) float64 960kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:24.120642
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
    • chain: 4
    • draw: 500
    • match: 60
  • Coordinates: (5)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • match

      (match)

      <U16

      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype='<U16')

    • home_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

    • away_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

  • Data variables: (2)

    • home_points

      (chain, draw, match)

      float64

      ...

      [120000 values with dtype=float64]

    • away_points

      (chain, draw, match)

      float64

      ...

      [120000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:24.120642

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• log_prior

  <xarray.Dataset> Size: 260kB
  Dimensions:    (chain: 4, draw: 500, team: 6)
  Coordinates:
    * chain      (chain) int64 32B 0 1 2 3
    * draw       (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team       (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      home_new   (chain, draw) float64 16kB ...
      sd_att     (chain, draw) float64 16kB ...
      sd_def     (chain, draw) float64 16kB ...
      intercept  (chain, draw) float64 16kB ...
      atts_star  (chain, draw, team) float64 96kB ...
      defs_star  (chain, draw, team) float64 96kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:24.377610
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
    • chain: 4
    • draw: 500
    • team: 6
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • team

      (team)

      <U8

      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype='<U8')

  • Data variables: (6)

    • home_new

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • sd_att

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • sd_def

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • intercept

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • atts_star

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

    • defs_star

      (chain, draw, team)

      float64

      ...

      [12000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:24.377610

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• sample_stats

  <xarray.Dataset> Size: 248kB
  Dimensions:                (chain: 4, draw: 500)
  Coordinates:
    * chain                  (chain) int64 32B 0 1 2 3
    * draw                   (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499
  Data variables: (12/17)
      max_energy_error       (chain, draw) float64 16kB ...
      index_in_trajectory    (chain, draw) int64 16kB ...
      smallest_eigval        (chain, draw) float64 16kB ...
      perf_counter_start     (chain, draw) float64 16kB ...
      largest_eigval         (chain, draw) float64 16kB ...
      step_size              (chain, draw) float64 16kB ...
      ...                     ...
      reached_max_treedepth  (chain, draw) bool 2kB ...
      perf_counter_diff      (chain, draw) float64 16kB ...
      tree_depth             (chain, draw) int64 16kB ...
      process_time_diff      (chain, draw) float64 16kB ...
      step_size_bar          (chain, draw) float64 16kB ...
      energy                 (chain, draw) float64 16kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:23.854033
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9
      sampling_time:              8.503105401992798
      tuning_steps:               1000

  • Dimensions:
  • Coordinates: (2)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

  • Data variables: (17)

    • max_energy_error

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • index_in_trajectory

      (chain, draw)

      int64

      ...

      [2000 values with dtype=int64]

    • smallest_eigval

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • perf_counter_start

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • largest_eigval

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • step_size

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • n_steps

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • lp

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • diverging

      (chain, draw)

      bool

      ...

      [2000 values with dtype=bool]

    • energy_error

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • acceptance_rate

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • reached_max_treedepth

      (chain, draw)

      bool

      ...

      [2000 values with dtype=bool]

    • perf_counter_diff

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • tree_depth

      (chain, draw)

      int64

      ...

      [2000 values with dtype=int64]

    • process_time_diff

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • step_size_bar

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • energy

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

  • Indexes: (2)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

  • Attributes: (6)

    created_at :

    2024-03-06T20:46:23.854033

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

    sampling_time :

    8.503105401992798

    tuning_steps :

    1000

  
  
• prior

  <xarray.Dataset> Size: 116kB
  Dimensions:    (chain: 1, draw: 500, team: 6)
  Coordinates:
    * chain      (chain) int64 8B 0
    * draw       (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team       (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      atts_star  (chain, draw, team) float64 24kB ...
      sd_att     (chain, draw) float64 4kB ...
      atts       (chain, draw, team) float64 24kB ...
      sd_def     (chain, draw) float64 4kB ...
      defs       (chain, draw, team) float64 24kB ...
      intercept  (chain, draw) float64 4kB ...
      home_new   (chain, draw) float64 4kB ...
      defs_star  (chain, draw, team) float64 24kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:09.475945
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
    • chain: 1
    • draw: 500
    • team: 6
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • team

      (team)

      <U8

      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype='<U8')

  • Data variables: (8)

    • atts_star

      (chain, draw, team)

      float64

      ...

      [3000 values with dtype=float64]

    • sd_att

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

    • atts

      (chain, draw, team)

      float64

      ...

      [3000 values with dtype=float64]

    • sd_def

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

    • defs

      (chain, draw, team)

      float64

      ...

      [3000 values with dtype=float64]

    • intercept

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

    • home_new

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

    • defs_star

      (chain, draw, team)

      float64

      ...

      [3000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:09.475945

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• prior_predictive

  <xarray.Dataset> Size: 492kB
  Dimensions:      (chain: 1, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 8B 0
    * draw         (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
    * match        (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
      home_team    (match) <U8 2kB ...
      away_team    (match) <U8 2kB ...
  Data variables:
      away_points  (chain, draw, match) int64 240kB ...
      home_points  (chain, draw, match) int64 240kB ...
  Attributes:
      created_at:                 2024-03-06T20:46:09.479330
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
    • chain: 1
    • draw: 500
    • match: 60
  • Coordinates: (5)

    • chain

      (chain)

      int64

      0

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • match

      (match)

      <U16

      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype='<U16')

    • home_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

    • away_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

  • Data variables: (2)

    • away_points

      (chain, draw, match)

      int64

      ...

      [30000 values with dtype=int64]

    • home_points

      (chain, draw, match)

      int64

      ...

      [30000 values with dtype=int64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:09.479330

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• observed_data

  <xarray.Dataset> Size: 9kB
  Dimensions:      (match: 60)
  Coordinates:
    * match        (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
      home_team    (match) <U8 2kB ...
      away_team    (match) <U8 2kB ...
  Data variables:
      home_points  (match) int64 480B ...
      away_points  (match) int64 480B ...
  Attributes:
      created_at:                 2024-03-06T20:46:09.480812
      arviz_version:              0.17.0
      inference_library:          pymc
      inference_library_version:  5.10.4+7.g34d2a5d9

  • Dimensions:
  • Coordinates: (3)

    • match

      (match)

      <U16

      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype='<U16')

    • home_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

    • away_team

      (match)

      <U8

      ...

      [60 values with dtype=<U8]

  • Data variables: (2)

    • home_points

      (match)

      int64

      ...

      [60 values with dtype=int64]

    • away_points

      (match)

      int64

      ...

      [60 values with dtype=int64]

  • Indexes: (1)

    • PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (4)

    created_at :

    2024-03-06T20:46:09.480812

    arviz_version :

    0.17.0

    inference_library :

    pymc

    inference_library_version :

    5.10.4+7.g34d2a5d9

  
  
• unconstrained_posterior

  <xarray.Dataset> Size: 36kB
  Dimensions:  (chain: 4, draw: 500)
  Coordinates:
    * chain    (chain) int64 32B 0 1 2 3
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
  Data variables:
      sd_att   (chain, draw) float64 16kB ...
      sd_def   (chain, draw) float64 16kB ...
  Attributes:
      sd_att:   pymc.logprob.transforms.LogTransform
      sd_def:   pymc.logprob.transforms.LogTransform

  • Dimensions:
  • Coordinates: (2)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

  • Data variables: (2)

    • sd_att

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • sd_def

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

  • Indexes: (2)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

  • Attributes: (2)

    sd_att :

    pymc.logprob.transforms.LogTransform

    sd_def :

    pymc.logprob.transforms.LogTransform

  
  

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