targetFun: Target Distributions

In thq80/Cornuet_2012_Adaptive-Mutiple-IS: A R Adaptive Multiple Importance Sampling

Description

Functions returning a target distribution. These function provide an example of implementation for three interesting targets. They use a function closure approach in order to accept a general set of arguments and to store in their environment the variables specified by the user. The returned function must be in the form

target(x)

Arguments

x	A p-dimensional observation

Value

The value of the log-density.

Example of Target Distributions

targetBanana(sig=100,b=0.01,log=TRUE):

Banana Shaped Target from Haario et al. (1999):

sig

Variance of x_2;

b

"Banana-ness" parameter. Moderate banana shape: b=0.01. Strong banana shape: b=0.03.

log

logical: should the log-density be returned.

targetMVN(Mu=rep(0,2),Sigma=diag(1,2,2)):

Multivariate Normal Target:

Mu

Vector of Means;

Sigma

Covariance Matrix.

targetMix(alpha=rep(.5,2),Mu=matrix(0,2,2),Sigma=array(cbind(diag(1,2,2),diag(1,2,2)),dim=c(2,2,2))):

Gaussian Mixture Target:

alpha

Vector of Mixture probabilities;

Mu

Matrix of Means (by row);

Sigma

Array of Covariance Matrices.

Author(s)

Luca Pozzi, [e-mail]

References

Haario, H., Saksman, E., and Tamminen, J. (1999). Adaptive proposal distribution for random walk Metropolis algorithm. Computational Statistics, 14:375-395.

See Also

See also proposal. For more details see the tutorial in vignette("demoARAMIS").

Examples

targetBanana()(c(1,2))
targetMVN()(c(1,2))
targetMix()(c(1,2))

thq80/Cornuet_2012_Adaptive-Mutiple-IS documentation built on May 21, 2019, 9:23 a.m.