On learning strategies for evolutionary Monte Carlo

Authors:
Gopi Goswami;Jun S. Liu
Affiliations:
Department of Statistics, Harvard University, Cambridge, USA;Department of Statistics, Harvard University, Cambridge, USA
Venue:
Statistics and Computing
Year:
2007

Citing 4
Cited 5

Convergence of adaptive direction sampling

Journal of Multivariate Analysis
An improved acceptance procedure for the hybrid Monte Carlo algorithm

Journal of Computational Physics
A Guide to Monte Carlo Simulations in Statistical Physics

A Guide to Monte Carlo Simulations in Statistical Physics
Monte Carlo Strategies in Scientific Computing

Monte Carlo Strategies in Scientific Computing

On population-based simulation for static inference

Statistics and Computing
Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Statistics and Computing
Evolutionary monte carlo based techniques for first passage time problems in credit risk and other applications in finance

EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
Adaptive Markov chain Monte Carlo for auxiliary variable method and its application to parallel tempering

Neural Networks
Improved short adjacent repeat identification using three evolutionary Monte Carlo schemes

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The real-parameter evolutionary Monte Carlo algorithm (EMC) has been proposed as an effective tool both for sampling from high-dimensional distributions and for stochastic optimization (Liang and Wong, 2001). EMC uses a temperature ladder similar to that in parallel tempering (PT; Geyer, 1991). In contrast with PT, EMC allows for crossover moves between parallel and tempered MCMC chains. In the context of EMC, we introduce four new moves, which enhance its efficiency as measured by the effective sample size. Secondly, we introduce a practical strategy for determining the temperature range and placing the temperatures in the ladder used in EMC and PT. Lastly, we prove the validity of the conditional sampling step of the snooker algorithm, a crossover move in EMC, which extends a result of Roberts and Gilks (1994).