evolMonteCarlo {EMC}R Documentation

evolutionary Monte Carlo algorithm

Description

Given a multi-modal and multi-dimensional target density function, a (possibly asymmetric) proposal distribution and a temperature ladder, this function produces samples from the target using the evolutionary Monte Carlo algorithm.

Usage

evolMonteCarlo(nIters,
               temperLadder,                       
               startingVals,                       
               logTarDensFunc,                     
               MHPropNewFunc,                      
               logMHPropDensFunc = NULL,           
               MHBlocks          = NULL,
               MHBlockNTimes     = NULL,
               moveProbsList     = NULL,               
               moveNTimesList    = NULL,              
               SCRWMNTimes       = NULL,                     
               SCRWMPropSD       = NULL,                         
               levelsSaveSampFor = NULL,           
               saveFitness       = FALSE,                
               verboseLevel      = 0,
               ...)                   

Arguments

Below sampDim refers to the dimension of the sample space, temperLadderLen refers to the length of the temperature ladder, and levelsSaveSampForLen refers to the length of the levelsSaveSampFor.

nIters integer > 0.
temperLadder double vector with all positive entries, in decreasing order.
startingVals double matrix of dimension temperLadderLen x sampDim or vector of length sampDim, in which case the same starting values are used for every temperature level.
logTarDensFunc function of two arguments (draw, ...) that returns the target density evaluated in the log scale.
MHPropNewFunc function of four arguments (temperature, block, currentDraw, ...) that returns new Metropolis-Hastings proposals. See details below on the argument block.
logMHPropDensFunc function of five arguments (temperature, block, currentDraw, proposalDraw, ...) that returns the proposal density evaluated in the log scale. See details below on the argument block.
MHBlocks list of integer vectors giving dimensions to be blocked together for sampling. It defaults to as.list(1:sampDim), i.e., each dimension is treated as a block on its own. See details below for an example.
MHBlockNTimes integer vector of number of times each block given by MHBlocks should be sampled in each iteration. It defaults to rep(1, length(MHBlocks)). See details below for an example.
moveProbsList named list of probabilities adding upto 1.
moveNTimesList named list of integers >= 0.
SCRWMNTimes integer > 0.
SCRWMPropSD double > 0.
levelsSaveSampFor integer vector with positive entries.
saveFitness logical.
verboseLevel integer, a value >= 2 produces a lot of output.
... optional arguments to be passed to logTarDensFunc, MHPropNewFunc and logMHPropDensFunc.

Details

MHPropNewFunc and logMHPropDensFunc
The MHPropNewFunc and the logMHPropDensFunc are called multiple times by varying the block argument over 1:length(MHBlocks), so these functions should know how to generate a proposal from the currentDraw or to evaluate the proposal density depending on which block was passed as the argument. See the example section for sample code.
MHBlocks and MHBlockNTimes
Blocking is an important and useful tool in MCMC that helps speed up sampling and hence mixing. Example: Let sampDim = 6. Let we want to sample dimensions 1, 2, 4 as one block, dimensions 3 and 5 as another and treat dimension 6 as the third block. Suppose we want to sample the three blocks mentioned above 1, 5 and 10 times in each iteration, respectively. Then we could set MHBlocks = list(c(1, 2, 4), c(3, 5), 6) and MHBlockNTimes = c(1, 5, 10).
The EMC and the TOEMC algorithm
The evolutionary Monte Carlo (EMC; Liang and Wong, 2001) algorithm is composed of the following moves:
MH Metropolis-Hastings or mutation
RC real crossover
SC snooker crossover
RE (random) exchange

The target oriented EMC (TOEMC; Goswami and Liu, 2007) algorithm has the following additional moves on top of EMC:

BCE best chromosome exchange
BIRE best importance ratio exchange
BSE best swap exchange
CE cyclic exchange

The current function could be used to run both EMC and TOEMC algorithms by specifying what moves to employ using the following variables.

moveProbsList and moveNTimesList
The allowed names for components of moveProbsList and moveNTimesList come from the abbreviated names of the moves above. For example, the following specifications are valid:
moveProbsList = list(MH = 0.4,
                     RC = 0.3,
                     SC = 0.3)

moveNTimesList = list(MH = 1, 
                      RC = floor(temperLadderLen / 2),
                      SC = floor(temperLadderLen / 2),
                      RE = temperLadderLen)           

SCRWMNTimes and SCRWMPropSD
The conditional sampling step of the snooker crossover (SC) move is done using random walk Metropolis (RWM) with normal proposals; SCRWMNTimes and SCRWMPropSD are the number of RWM draws and the proposal standard deviation for the RWM step, respectively. Note these variables are only required if the SC move has positive probability in moveProbsList or a positive number of times in moveNTimesList.

levelsSaveSampFor
By default, samples are saved and returned for temperature level temperLadderLen. The levelsSaveSampFor could be used to save samples from other temperature levels as well (e.g., levelsSaveSampFor = 1:temperLadderLen saves samples from all levels).
saveFitness
The term fitness refers to the function H(x), where the target density of interest is given by:

g(x) propto exp[ -H(x) / tau_{min} ]

H(x) is also known as the energy function. By default, the fitness values are not saved, but one can do so by setting saveFitness = TRUE.

Value

This function returns a list with the following components:

draws array of dimension nIters x sampDim x levelsSaveSampForLen, if saveFitness = FALSE. If saveFitness = TRUE, then the returned array is of dimension nIters x (sampDim + 1) x levelsSaveSampForLen; i.e., each of the levelsSaveSampForLen matrices contain the fitness values in their last column.
acceptRatios matrix of the acceptance rates for various moves used.
detailedAcceptRatios list of matrices with detailed summary of the acceptance rates for various moves used.
nIters the nIters argument.
temperLadder the temperLadder argument.
startingVals the startingVals argument.
moveProbsList the moveProbsList argument.
moveNTimesList the moveNTimesList argument.
levelsSaveSampFor the levelsSaveSampFor argument.
time the time taken by the run.

Note

The effect of leaving the default value NULL for some of the arguments above are as follows:

logMHPropDensFunc the proposal density MHPropNewFunc is deemed symmetric.

MHBlocks

as.list(1:sampDim).

MHBlockNTimes

rep(1, length(MHBlocks)).

moveProbsList

list(MH = 0.4, RC = 0.3, SC = 0.3).

moveNTimesList

list(MH = 1, RC = mm, SC = mm, RE = nn), where

mm <- floor(nn / 2) and nn <- temperLadderLen.

SCRWMNTimes

1, if SC is used.

SCRWMPropSD

needs to be provided by the user, if SC is used.

levelsSaveSampFor

temperLadderLen.

Author(s)

Gopi Goswami goswami@stat.harvard.edu

References

Gopi Goswami and Jun S. Liu (2007). On learning strategies for evolutionary Monte Carlo. Statistics and Computing 17:1:23-38.

Faming Liang and Wing H.Wong (2001). Real-Parameter Evolutionary Monte Carlo with Applications to Bayesian Mixture Models. Journal of the American Statistical Association 96:653-666.

See Also

parallelTempering

Examples

samplerObj <-
    with(VShapedFuncGenerator(-13579),
     {
         allMovesNTimesList <- list(MH = 1, RC = 2, SC = 2, RE = 4,
                                    BIRE = 2, BCE = 2, BSE = 2, CE = 2)
         evolMonteCarlo(nIters            = 2000,
                        temperLadder      = c(15, 6, 2, 1),
                        startingVals      = c(0, 0),
                        logTarDensFunc    = logTarDensFunc,
                        MHPropNewFunc     = MHPropNewFunc,
                        moveNTimesList    = allMovesNTimesList,
                        SCRWMNTimes       = 1,
                        SCRWMPropSD       = 3.0,
                        levelsSaveSampFor = seq_len(4),
                        verboseLevel      = 1)
     })
print(samplerObj)
print(names(samplerObj))
with(samplerObj,
 {
     print(detailedAcceptRatios)
     print(dim(draws))
     par(mfcol = c(2, 2))
     for (ii in seq_along(levelsSaveSampFor)) {
         main <- paste('temper:', round(temperLadder[levelsSaveSampFor[ii]], 3))
         plot(draws[ , , ii],
              xlim = c(-5, 20),
              ylim = c(-8, 8),
              pch  = '.',
              ask  = FALSE,
              main = as.expression(main),
              xlab = as.expression(substitute(x[xii], list(xii = 1))),
              ylab = as.expression(substitute(x[xii], list(xii = 2))))
     }
 })

[Package EMC version 1.1 Index]