Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/105324
Title: Real-Parameter Evolutionary Monte Carlo with Applications to Bayesian Mixture Models
Authors: Liang, F. 
Wong, W.H.
Keywords: Crossover
Evolutionary Monte Carlo
Exchange
Genetic algorithm
Markov chain Monte Carlo
Metropolis algorithm
Mixture model
Mutation
Neural network
Parallel tempering
Issue Date: Jun-2001
Citation: Liang, F.,Wong, W.H. (2001-06). Real-Parameter Evolutionary Monte Carlo with Applications to Bayesian Mixture Models. Journal of the American Statistical Association 96 (454) : 653-666. ScholarBank@NUS Repository.
Abstract: We propose an evolutionary Monte Carlo algorithm to sample from a target distribution with real-valued parameters. The attractive features of the algorithm include the ability to learn from the samples obtained in previous steps and the ability to improve the mixing of a system by sampling along a temperature ladder. The effectiveness of the algorithm is examined through three multimodal examples and Bayesian neural networks. The numerical results confirm that the real-coded evolutionary algorithm is a promising general approach for simulation and optimization.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/105324
ISSN: 01621459
Appears in Collections:Staff Publications

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