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optismixture (version 0.1)

Optimal Mixture Weights in Multiple Importance Sampling

Description

Code for optimal mixture weights in importance sampling. Workhorse functions penoptpersp() and penoptpersp.alpha.only() minimize estimated variances with and without control variates respectively. It can be used in adaptive mixture importance sampling, for example, function batch.estimation() does two stages, a pilot estimate of mixing alpha and a following importance sampling.

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Version

Install

install.packages('optismixture')

Monthly Downloads

18

Version

0.1

License

GPL-2

Maintainer

Hera He

Last Published

August 25th, 2015

Functions in optismixture (0.1)

do.plain.mc

Do plain monte carlo with target density
get.index.b

Internal function. Get the row index in the stacked sample matrices for the $b^{th}$ batch
alpha2N

Internal function. convert mixture proportions to mixture sample size with a fixed total sample size
batch.estimation

Two stage estimation, a pilot estimate of mixing alpha and a following importance sampling, with or without control variates
get.var

Internal function. With stratified samples, calculate the variance of the estimate from importance sampling without control variates
penoptpersp

penalized optimization of the constrained linearized perspective function
compatible.test

Test the compatibility of user defined functions fname, rpname, rqname, dpname, dqname with mixture.param
optismixture

Optimal Mixture Weights in Multiple Importance Sampling
mixture.is.estimation

For a given mixture weight alpha, use importance sample with or withour control variates for estimation
penoptpersp.alpha.only

penalized optimization of the constrained linearized perspective function
get.initial.alpha

Internal function. Calculate the initial alpha vector for the optimization of alpha with a lower bound constraint
do.mixture.sample

Internal function. sample from the mixture distribution $q_{\alpha}$