bebi103.hmc.check_variance

bebi103.hmc.check_variance(samples, epsilon=1e-10, omit=(), omit_array_entry=())

Compute the variance of each variable for each chain and report True if the variance if nonzero and False if the variance is zero.

Parameters:

  • samples (cmdstanpy.CmdStanMCMC instance or dict) – MCMC samples from which to compute split Rhat, either as a cmdstanpy.CmdStanMCMC instance or as a dictionary of two-dimensional arrays for each expectand. For the dictionary, the first dimension of each element indexes the Markov chains and the second dimension indexes the sequential states within each Markov chain.

  • epsilon (float, default 1e-10) – Small number, below which a variance is considered to be zero.

  • omit (str, re.Pattern, or iterable thereof) – Glob pattern(s) matched against the base (non-indexed) name of each variable. Any variable whose base name matches an entry is omitted, whether scalar or array valued. For example, omit=’*_pred’ omits every variable whose name ends in ‘_pred’. Compiled re.Pattern entries are matched as regular expressions. A single string or pattern may be given instead of an iterable.

  • omit_array_entry (str or iterable of str) – Specific array entries to omit, e.g. ‘y_pred[1]’ or ‘beta[1,2]’. Each entry must include bracketed, comma-separated integer indices. A single string may be given instead of an iterable.

Returns:

output – Each key is the name of a variable and each value is a Numpy array whose length is equal to the number of chains in the sampling. Entry i is True if the variance is nonzero and False otherwise.

Return type:

dict