expt.upts: Expected number of unique inputs after the final IMIS...

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 expected number of unique points after re-sampling

Usage

expt.upts(w, m)

Arguments

w	A vector of importance weights corresponding to each row of the mixture of the prior and multivariate gaussian distributions
m	The final re-sample size

Value

A scalar describing the number of unique points from the final re-sample

Note

For use in the function final.resamp

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.

Examples

#Generate a vector of weights#
wts <- runif(100, 0, .99)
expt.upts(wts, m = 3000)

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