do.plain.mc: Do plain monte carlo with target density In optismixture: Optimal Mixture Weights in Multiple Importance Sampling

Description

Do plain monte carlo with target density

Usage

 1 do.plain.mc(plainmc.N, mixture.param, fname = "f", rpname = "rp") 

Arguments

 plainmc.N number of samples mixture.param mixture.param = list(p, J, ...), where p is the dimension of the sample, and J is the number of mixture components, including the defensive one. mixture.param should be compatible with user defined functions f(n, j, mixture.param), rp(n, mixture.param), rq(n, j, mixture.param), dp(xmat, mixture.param), dq(xmat, j, mixture.param) fname name of user defined function fname(xmat, j, mixture.param). xmat is an n \times p matrix of n samples with p dimensions. fname returns a vector of function values for each row in xmat. fname is defined for j = 1, \cdots, J. j = 1, \cdots, J - 1 corresponds to different proposal mixture components, and j = J corresponds to the defensive mixture component. rpname name of user definded function rpname(n, mixture.param). It generates n random samples from target distribution pname. Parameters can be specified in mixture.param. rpname returns an n \times p matrix.

Value

a list of

plainmc.N

number of samples for the plain monte carlo

mu.hat

estimated E_p f from plain monte carlos samples

sd.hat

estimated sd for mu.hat

optismixture documentation built on May 29, 2017, 1:02 p.m.