SelectMinED: Select Minimum Energy Design samples from a candidate set

In mined: Minimum Energy Designs

Select Minimum Energy Design samples from a candidate set

Description

Select MinED samples from candidates by optimizing the generalized MinED criterion in Joseph et al. (2019).

Usage

SelectMinED(candidates, candlf, n, gamma=1, s=2)

Arguments

candidates	Candidate samples from the target distribution, which can be MC, QMC, or MCMC samples.
candlf	The log-unnormalized density function values corresponding to the candidates.
n	The required number of MinED samples.
gamma	The parameter in the anealled version of density function. Optional, default is “1”.
s	The parameter in generalized distance. Optional, default is “2”.

Details

This function select MinED samples from a given set of candidate samples. The function is used internally in the mined function repeatedly for K times, where K is the number of annealing steps in the algorithm. Refer to Joseph et al., (2018) for more details.

Value

The value returned from the function is a list containing the following components:

points	The MinED samples selected from the candidates.
logf	The log-unnormalized density function values of the points.

Author(s)

Dianpeng Wang <[e-mail]> and V. Roshan Joseph <[e-mail]>

References

Joseph, V. R., Wang, D., Gu, L., Lv, S., and Tuo, R. (2019). "Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs", Technometrics, 61, 297-308, arXiv:1712.08929, DOI:10.1080/00401706.2018.1552203.

See Also

mined

Examples

cand <- matrix(runif(10000, min = -4, max = 4), ncol = 1)
candlf <- log(dnorm(cand))
res <- mined::SelectMinED(cand, as.vector(candlf), 150, 1.0, 2.0)
print(res)
par(mfrow=c(1,2))
hist(cand)
hist(res$points)

mined documentation built on June 27, 2022, 1:06 a.m.