Truncated importance sampling (TIS)
Implementation of truncated (self-normalized) importance sampling (TIS), truncated at S^(1/2) as recommended by Ionides (2008).
tis(log_ratios, ...)
## S3 method for class 'array'
tis(log_ratios, ..., r_eff = NULL, cores = getOption("mc.cores", 1))
## S3 method for class 'matrix'
tis(log_ratios, ..., r_eff = NULL, cores = getOption("mc.cores", 1))
## Default S3 method:
tis(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 tis() methods return an object of class "tis",
which is a named list with the following components:
log_weightsVector or matrix of smoothed (and truncated) but unnormalized log
weights. To get normalized weights use the
weights() method provided for objects of
class tis.
diagnosticsA named list containing one vector:
pareto_k: Not used in tis, all set to 0.
n_eff: Effective sample size estimates.
Objects of class "tis" 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 tis.
dimsInteger vector of length 2 containing S (posterior sample size)
and N (number of observations).
methodMethod used for importance sampling, here tis.
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).
Ionides, Edward L. (2008). Truncated importance sampling. Journal of Computational and Graphical Statistics 17(2): 295–311.
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)) tis_result <- tis(log_ratios, r_eff = r_eff) str(tis_result) # extract smoothed weights lw <- weights(tis_result) # default args are log=TRUE, normalize=TRUE ulw <- weights(tis_result, normalize=FALSE) # unnormalized log-weights w <- weights(tis_result, log=FALSE) # normalized weights (not log-weights) uw <- weights(tis_result, log=FALSE, normalize = FALSE) # unnormalized weights
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