entropy.wts: Entropy of the rescaled weights relative to uniformity

In HPbayes: Heligman Pollard mortality model parameter estimation using Bayesian Melding with Incremental Mixture Importance Sampling

Description

Performance measure for the IMIS algorithm that calculates the entropy of the importance weights relative to uniformity.

Usage

entropy.wts(w)

Arguments

w	A vector of importance weights corresponding to each row of the mixture of the prior and multivariate gaussian draws

Value

Vector of entropy values relative to uniformity for a vector of weights

Note

For use in the function final.resamp from the package HPbayes

References

Raftery, Adrian and Le Bao. 2009. "Estimating and Projecting Trends in HIV/AIDS Gen- eralized Epidemics Using Incremental Mixture Importance Sampling." Technical Report 560, Department of Statistics, University of Washington.

Poole, David and Adrian Raftery. 2000. "Inference for Deterministic Simulation Models: The Bayesian Melding Approach." Journal of the American Statistical Association 95:1244–1255.

Examples

#Generate a vector of weights#
wts <- runif(100, 0, .99)
entropy.wts(w=wts)

HPbayes documentation built on May 2, 2019, 5:53 a.m.