default.itemps: Default Sigmoidal, Harmonic and Geometric Temperature Ladders

In tgp: Bayesian Treed Gaussian Process Models

Default Sigmoidal, Harmonic and Geometric Temperature Ladders

Description

Parameterized by the minimum desired inverse temperature, this function generates a ladder of inverse temperatures k[1:m] starting at k[1] = 1, with m steps down to the final temperature k[m] = k.min progressing sigmoidally, harmonically or geometrically. The output is in a format convenient for the b* functions in the tgp package (e.g. btgp), including stochastic approximation parameters c0 and n0 for tuning the uniform pseudo-prior output by this function

Usage

default.itemps(m = 40, type = c("geometric", "harmonic","sigmoidal"),
               k.min = 0.1, c0n0 = c(100, 1000), lambda = c("opt",
               "naive", "st"))

Arguments

m	Number of temperatures in the ladder; m=1 corresponds to importance sampling at the temperature specified by k.min (in this case all other arguments are ignored)
type	Choose from amongst two common defaults for simulated tempering and Metropolis-coupled MCMC, i.e., geometric (default) or harmonic, or a sigmoidal ladder (default) that concentrates more inverse temperatures near 1
k.min	Minimum inverse temperature desired
c0n0	Stochastic approximation parameters used to tune the simulated tempering pseudo-prior ($pk) to get a uniform posterior over the inverse temperatures; must be a 2-vector of positive integers c(c0, n0); see the Geyer & Thompson reference below
lambda	Method for combining the importance samplers at each temperature. Optimal combination ("opt") is the default, weighting the IS at each temperature k by lambda[k] = sum(w[k,]))^2/sum(w[k,]^2). Setting lambda = "naive" allows each temperature to contribute equally (λ[k] = 1, or equivalently ignores delineations due to temperature when using importance weights. Setting lambda = "st" allows only the first (cold) temperature to contribute to the estimator, thereby implementing simulated tempering

Details

The geometric and harmonic inverse temperature ladders are usually defined by an index i = 1:m and a parameter delta > 0. The geometric ladder is defined by

k[i] = (1 + delta)^(1-i),

and the harmonic ladder by

k[i] = (1 + delta*(i-1))^(-1).

Alternatively, specifying the minimum temperature k.min in the ladder can be used to uniquely determine delta. E.g., for the geometric ladder

delta = k.min^(1/(1-m))-1,

and for the harmonic

delta = (k.min^(-1)-1)/(m-1).

In a similar spirit, the sigmoidal ladder is specified by first situating m indices j[i] in Re so that k[1] = k(j[1]) = 1 and k[m] = k(j[m]) = k.min under

k(j[i]) = 1.01 - 1/(1+exp(-j[i])).

The remaining j[2:(m-1)] are spaced evenly between j[i] and j[m] to fill out the ladder k[2:(m-1)] = k(j[2:(m-1)]).

For more details, see the Importance tempering paper cited below and a full demonstration in vignette("tgp2")

Value

The return value is a list which is compatible with the input argument itemps to the b* functions (e.g. btgp), containing the following entries:

c0n0	A copy of the c0n0 input argument
k	The generated inverse temperature ladder; a vector with length(k) = m containing a decreasing sequence from 1 down to k.min
pk	A vector with length(pk) = m containing an initial pseudo-prior for the temperature ladder of 1/m for each inverse temperature
lambda	IT method, as specified by the input argument

Author(s)

Robert B. Gramacy, [e-mail], and Matt Taddy, [e-mail]

References

Gramacy, R.B., Samworth, R.J., and King, R. (2010) Importance Tempering. ArXiV article 0707.4242 Statistics and Computing, 20(1), pp. 1-7; https://arxiv.org/abs/0707.4242.

For stochastic approximation and simulated tempering (ST):

Geyer, C.~and Thompson, E.~(1995). Annealing Markov chain Monte Carlo with applications to ancestral inference. Journal of the American Statistical Association, 90, 909–920.

For the geometric temperature ladder:

Neal, R.M.~(2001) Annealed importance sampling. Statistics and Computing, 11, 125–129

Justifying geometric and harmonic defaults:

Liu, J.S.~(1002) Monte Carlo Strategies in Scientific Computing. New York: Springer. Chapter 10 (pages 213 & 233)

https://bobby.gramacy.com/r_packages/tgp/

See Also

btgp

Examples

## comparing the different ladders
geo <- default.itemps(type="geometric")
har <- default.itemps(type="harmonic")
sig <- default.itemps(type="sigmoidal")
par(mfrow=c(2,1))
matplot(cbind(geo$k, har$k, sig$k), pch=21:23,
        main="inv-temp ladders", xlab="indx",
        ylab="itemp")
legend("topright", pch=21:23, 
       c("geometric","harmonic","sigmoidal"), col=1:3)
matplot(log(cbind(sig$k, geo$k, har$k)), pch=21:23,
        main="log(inv-temp) ladders", xlab="indx",
        ylab="itemp")

## Not run: 
## using Importance Tempering (IT) to improve mixing
## on the motorcycle accident dataset
library(MASS)
out.it <- btgpllm(X=mcycle[,1], Z=mcycle[,2], BTE=c(2000,22000,2),
        R=3, itemps=default.itemps(), bprior="b0", trace=TRUE, 
        pred.n=FALSE)

## compare to regular tgp w/o IT
out.reg <- btgpllm(X=mcycle[,1], Z=mcycle[,2], BTE=c(2000,22000,2),
        R=3, bprior="b0", trace=TRUE, pred.n=FALSE)

## compare the heights explored by the three chains:
## REG, combining all temperatures, and IT
p <- out.it$trace$post
L <- length(p$height)
hw <- suppressWarnings(sample(p$height, L, prob=p$wlambda, replace=TRUE))
b <- hist2bar(cbind(out.reg$trace$post$height, p$height, hw))
par(mfrow=c(1,1))
barplot(b, beside=TRUE, xlab="tree height", ylab="counts", col=1:3,
        main="tree heights encountered")
legend("topright", c("reg MCMC", "All Temps", "IT"), fill=1:3)

## End(Not run)

tgp documentation built on Jan. 7, 2023, 1:17 a.m.