Applies a burn in to and finds a point estimate for each of the chains outputted from ``runMCMCChains``.
processMCMCChains(
mcmc_lst,
burn,
point_estimate_method = "median",
construct_psm = FALSE
)A named list similar to the output of ``runMCMCChains`` with some additional entries:
* ``allocation_probability``: $(N x K)$ matrix. The point estimate of the allocation probabilities for each data point to each class.
* ``prob``: $N$ vector. The point estimate of the probability of being allocated to the class with the highest probability.
* ``pred``: $N$ vector. The predicted class for each sample.
Output from ``runMCMCChains``
The number of MCMC samples to drop as part of a burn in.
Summary statistic used to define the point estimate. Must be ``'mean'`` or ``'median'``. ``'median'`` is the default.
Logical indicating if PSMs be constructed in the unsupervised views. Defaults to FALSE. If TRUE the PSM is constructed and this is used to infer the point estimate rather than the sampled partitions.