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.
|Author||Hera Y. He, Art B. Owen|
|Date of publication||2015-08-25 19:20:56|
|Maintainer||Hera Y. He <email@example.com>|
|Package repository||View on CRAN|
|Installation||Install the latest version of this package by entering the following in R:
|alpha2N: Internal function. convert mixture proportions to mixture...|
|batch.estimation: Two stage estimation, a pilot estimate of mixing alpha and a...|
|compatible.test: Test the compatibility of user defined functions _fname,...|
|do.mixture.sample: Internal function. sample from the mixture distribution...|
|do.plain.mc: Do plain monte carlo with target density|
|get.index.b: Internal function. Get the row index in the stacked sample...|
|get.initial.alpha: Internal function. Calculate the initial alpha vector for the...|
|get.var: Internal function. With stratified samples, calculate the...|
|mixture.is.estimation: For a given mixture weight alpha, use importance sample with...|
|optismixture: Optimal Mixture Weights in Multiple Importance Sampling|
|penoptpersp: penalized optimization of the constrained linearized...|
|penoptpersp.alpha.only: penalized optimization of the constrained linearized...|
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