A RetroSearch Logo

Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Search Query:

Showing content from http://reference.wolfram.com/language/guide/ProbabilityAndStatistics.html below:

Probability & Statistics—Wolfram Language Documentation

Probability & Statistics—Wolfram Language Documentation WOLFRAM Products

More mobile apps

Core Technologies of Wolfram Products Deployment Options From the Community Consulting & Solutions

We deliver solutions for the AI era—combining symbolic computation, data-driven insights and deep technology expertise.

WolframConsulting.com

Wolfram Solutions

More Wolfram Solutions

Wolfram Solutions For Education

More Solutions for Education

Learning & Support Get Started More Learning Grow Your Skills Tech Support Company Work with Us Educational Programs for Adults Educational Programs for Youth Read Educational Resources Wolfram Initiatives Events Wolfram|Alpha Wolfram Cloud Your Account Search Navigation Menu Wolfram Language & System Documentation Center Wolfram Language Home Page » GUIDE Probability & Statistics

Probability and statistics are used to model uncertainty from a variety of sources, such as incomplete or simplified models. Yet you can build useful models for aggregate or overall behavior of the system in question. These types of models are now universally used across all areas of science, technology, and business.  The Wolfram Language uses symbolic distributions and processes as models for random variables and random processes. The models can be automatically computed from data or analytically constructed from a rich library of built-in distributions and processes. The models can be simulated or used to directly answer a variety of questions.

Probability compute probabilities of predicates

Expectation compute expectations of expressions

NProbability  ▪  NExpectation  ▪  Distributed ()  ▪  Conditioned ()

Random Variables »

RandomVariate generate random variates from a distribution

EstimatedDistribution estimate parametric or derived distribution from data

DistributionFitTest test how well data and a distribution fit

PDF  ▪  CDF  ▪  Mean  ▪  Variance  ▪  Around

Distributions

NormalDistribution parametric distributions ...

SmoothKernelDistribution nonparametric distributions ...

TransformedDistribution derived distributions ...

Random Processes »

RandomFunction simulate a random process

TemporalData represent one or several time-series datasets

EstimatedProcess estimate process parameters from data

SliceDistribution  ▪  Mean  ▪  CovarianceFunction

Processes

PoissonProcess parametric processes ...

ARMAProcess time series processes ...

ItoProcess stochastic differential equation processes ...

Spatial Point Processes & Statistics »

RandomPointConfiguration simulate a random point process

SpatialPointData spatial annotated point data

EstimatedPointProcess estimate point process from data

PointDensity  ▪  RipleyK  ▪  SpatialRandomnessTest  ▪  ...

Point Processes »

PoissonPointProcess  ▪  HardcorePointProcess  ▪  MaternPointProcess  ▪  ...

Survival Analysis »

EventData represent censored and truncated data

Median  ▪  SurvivalModelFit  ▪  CoxModelFit

Reliability Analysis »

ReliabilityDistribution reliability block diagram-based lifetime distribution

FailureDistribution  ▪  BirnbaumImportance

Related Guides Top

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4