Computes marginal likelihood in Gaussian graphical models through a novel telescoping block decomposition of the precision matrix which allows estimation of model evidence. The top level function used to estimate marginal likelihood is called evidence(), which expects the prior name, data, and relevant prior specific parameters. This package also provides an MCMC prior sampler using the same underlying approach, implemented in prior_sampling(), which expects a prior name and prior specific parameters. Both functions also expect the number of burn-in iterations and the number of sampling iterations for the underlying MCMC sampler.
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