Optimal Mixture Weights in Multiple Importance Sampling

Documentation for package ‘optismixture’ version 0.1

optismixture-package	Optimal Mixture Weights in Multiple Importance Sampling
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
compatible.test	Test the compatibility of user defined functions _fname, rpname, rqname, dpname, dqname_ with _mixture.param_
do.mixture.sample	Internal function. sample from the mixture distribution q_{alpha}
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
get.initial.alpha	Internal function. Calculate the initial alpha vector for the optimization of _alpha_ with a lower bound constraint
get.var	Internal function. With stratified samples, calculate the variance of the estimate from importance sampling without control variates
mixture.is.estimation	For a given mixture weight alpha, use importance sample with or withour control variates for estimation
optismixture	Optimal Mixture Weights in Multiple Importance Sampling
penoptpersp	penalized optimization of the constrained linearized perspective function
penoptpersp.alpha.only	penalized optimization of the constrained linearized perspective function