isML: Marginal Likelihood by Importance Sampling for User-Written...

In erlisR/iLaplaceExamples: Extra Utilities and Examples for iLaplace

Description

The function computes the marginal likelihood by importance sampling and from user-written functions.

Usage

isML(logfun, nsim, theta.hat, tune, V, df, verbose)

Arguments

logfun	The logarithm of the objective function
nsim	The number of draws form the importance density
theta.hat	The center of the proposal
tune	A tunning value to be used to achieve the desired efficiency
V	The scale matrix of the importance density
df	The degrees of freedom of the importance density
verbose	A switch which determines whether or not the progress of the sampler is printed to the screen. If verbose is greater than 0 the iteration number, and importance sampling approximation are sent to the screen every verboseth iteration

Value

double, the logarithm of the marginal likelihood

References

Chib S. & Jeliazikov I. (2001). Marginal likelihood from the Metropolis-Hastings output. Journal of the American Statistical Association, 46, 270–281.

Robert C. P. & Casella G. (2004). Monte Carlo Statistical Methods. 2nd Edition. New York: Springer.

See Also

nlpost_gomp, nlpost_bod2 for examples; MHmcmc, ChibML

erlisR/iLaplaceExamples documentation built on May 16, 2019, 8:48 a.m.