Importance sampling of matrices
# S3 method for matrix
importance_sampling(
log_ratios,
method,
...,
r_eff = NULL,
cores = getOption("mc.cores", 1)
)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.
Arguments passed on to the various methods.
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 will be over-optimistic. See the relative_eff()
helper function for computing r_eff. If using psis with
draws of the log_ratios not obtained from MCMC then the warning
message thrown when not specifying r_eff can be disabled by
setting r_eff to NA.
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).
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).