Pareto smoothed importance sampling (PSIS) using approximate posteriors

Usage

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, ...)

Arguments

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 mc.cores which can be set for an entire R session by options(mc.cores = NUMBER). The old option loo.cores is now deprecated but will be given precedence over mc.cores until loo.cores is removed in a future release. As of version 2.0.0 the default is now 1 core if mc.cores is not set, but we recommend using as many (or close to as many) cores as possible.

Note for Windows 10 users: it is strongly recommended to avoid using the .Rprofile file to set mc.cores (using the cores argument or setting mc.cores interactively or in a script is fine).

Methods (by class)

ap_psis(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.
ap_psis(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.
ap_psis(default): A vector of length \(S\) (posterior sample size).