Standard importance sampling (SIS)
Implementation of standard importance sampling (SIS).
sis(log_ratios, ...)
## S3 method for class 'array'
sis(log_ratios, ..., r_eff = NULL, cores = getOption("mc.cores", 1))
## S3 method for class 'matrix'
sis(log_ratios, ..., r_eff = NULL, cores = getOption("mc.cores", 1))
## Default S3 method:
sis(log_ratios, ..., r_eff = NULL)log_ratios |
An array, matrix, or vector of importance ratios on the log scale (for Importance sampling 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. |
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 |
cores |
The number of cores to use for parallelization. This defaults to
the option
|
The sis() methods return an object of class "sis",
which is a named list with the following components:
log_weightsVector or matrix of smoothed but unnormalized log
weights. To get normalized weights use the
weights() method provided for objects of
class sis.
diagnosticsA named list containing one vector:
pareto_k: Not used in sis, all set to 0.
n_eff: effective sample size estimates.
Objects of class "sis" also have the following attributes:
norm_const_logVector of precomputed values of colLogSumExps(log_weights) that are
used internally by the weights method to normalize the log weights.
r_effIf specified, the user's r_eff argument.
tail_lenNot used for sis.
dimsInteger vector of length 2 containing S (posterior sample size)
and N (number of observations).
methodMethod used for importance sampling, here sis.
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).
Vehtari, A., Gelman, A., and Gabry, J. (2017a). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413–1432. doi:10.1007/s11222-016-9696-4 (journal version, preprint arXiv:1507.04544).
Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2019). Pareto smoothed importance sampling. preprint arXiv:1507.02646
psis() for approximate LOO-CV using PSIS.
loo() for approximate LOO-CV.
pareto-k-diagnostic for PSIS diagnostics.
log_ratios <- -1 * example_loglik_array() r_eff <- relative_eff(exp(-log_ratios)) sis_result <- sis(log_ratios, r_eff = r_eff) str(sis_result) # extract smoothed weights lw <- weights(sis_result) # default args are log=TRUE, normalize=TRUE ulw <- weights(sis_result, normalize=FALSE) # unnormalized log-weights w <- weights(sis_result, log=FALSE) # normalized weights (not log-weights) uw <- weights(sis_result, log=FALSE, normalize = FALSE) # unnormalized weights
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