model_likelihood_n: Computes the likelihood of a model from samples from its posterior distribution.
Description
The likelihood of a graph can be computed by integrating over all the graph parameters (with appropriate priors).
Doing this by sampling from priors is very inefficient, so we use samples from the posteriors to importance
sample the likelihood.
Samples of log likelihoods from the posterior distribution of the graph.
no_samples
Number of permutations to sample when computing the result.
Value
The likelihood of a graph where graph parameters are integrated out given as the mean and standard
deviation over no_samples different permutations of the input.
Details
The numerical issues with adding a lot of numbers in log space is unstable
so we get a better estimate by doing it several times on different permutations
of the data.This function calculates the mean of the likelihoods over different permutations of the
input and estimates the standard devition.