Learn R Programming

optismixture (version 0.1)

do.plain.mc: Do plain monte carlo with target density

Description

Do plain monte carlo with target density

Usage

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