ap_psis: Pareto smoothed importance sampling (PSIS) using approximate...

In jgabry/loo: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

Pareto smoothed importance sampling (PSIS) using approximate posteriors

Description

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).

jgabry/loo documentation built on Nov. 26, 2024, 5:29 p.m.