importance_sampling: A parent class for different importance sampling methods.

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

A parent class for different importance sampling methods.

Description

A parent class for different importance sampling methods.

Usage

importance_sampling(log_ratios, method, ...)

## S3 method for class 'array'
importance_sampling(
  log_ratios,
  method,
  ...,
  r_eff = 1,
  cores = getOption("mc.cores", 1)
)

## S3 method for class 'matrix'
importance_sampling(
  log_ratios,
  method,
  ...,
  r_eff = 1,
  cores = getOption("mc.cores", 1)
)

## Default S3 method:
importance_sampling(log_ratios, method, ..., r_eff = 1)

Arguments

log_ratios	An array, matrix, or vector of importance ratios on the log scale (for PSIS-LOO these are negative log-likelihood values). See the Methods (by class) section below for a detailed description of how to specify the inputs for each method.
method	The importance sampling method to use. The following methods are implemented: "psis": Pareto-Smoothed Importance Sampling (PSIS). Default method. "tis": Truncated Importance Sampling (TIS) with truncation at sqrt(S), where S is the number of posterior draws. "sis": Standard Importance Sampling (SIS).
...	Arguments passed on to the various methods.
r_eff	Vector of relative effective sample size estimates containing one element per observation. The values provided should be the relative effective sample sizes of 1/exp(log_ratios) (i.e., 1/ratios). This is related to the relative efficiency of estimating the normalizing term in self-normalizing importance sampling. If r_eff is not provided then the reported PSIS effective sample sizes and Monte Carlo error estimates can be over-optimistic. If the posterior draws are (near) independent then r_eff=1 can be used. r_eff has to be a scalar (same value is used for all observations) or a vector with length equal to the number of observations. The default value is 1. See the relative_eff() helper function for computing r_eff.
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).

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