Pareto smoothed importance sampling (PSIS) using approximate posteriors
Pareto smoothed importance sampling (PSIS) using approximate posteriors
ap_psis(log_ratios, log_p, log_g, ...)
## S3 method for class 'array'
ap_psis(log_ratios, log_p, log_g, ..., cores = getOption("mc.cores", 1))
## S3 method for class 'matrix'
ap_psis(log_ratios, log_p, log_g, ..., cores = getOption("mc.cores", 1))
## Default S3 method:
ap_psis(log_ratios, log_p, log_g, ...)log_ratios |
The log-likelihood ratios (ie -log_liks) |
log_p |
The log-posterior (target) evaluated at S samples from the proposal distribution (g). A vector of length S. |
log_g |
The log-density (proposal) evaluated at S samples from the proposal distribution (g). A vector of length S. |
... |
Currently not in use. |
cores |
The number of cores to use for parallelization. This defaults to
the option
|
array: An I by C by N array, where I
is the number of MCMC iterations per chain, C is the number of
chains, and N is the number of data points.
matrix: An S by N matrix, where S is the size
of the posterior sample (with all chains merged) and N is the number
of data points.
default: A vector of length S (posterior sample size).
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