Liang, Faming; Wong, Wing Hung Real-parameter evolutionary Monte Carlo with applications to Bayesian mixture models. (English) Zbl 1017.62022 J. Am. Stat. Assoc. 96, No. 454, 653-666 (2001). Summary: 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. Cited in 56 Documents MSC: 62F15 Bayesian inference 65C05 Monte Carlo methods 65C40 Numerical analysis or methods applied to Markov chains Keywords:crossover; parallel tempering; exchange; neural network; genetic algorithm; Markov chain Monte Carlo; Metropolis algorithm; mixture model; mutation; evolutionary Monte Carlo PDFBibTeX XMLCite \textit{F. Liang} and \textit{W. H. Wong}, J. Am. Stat. Assoc. 96, No. 454, 653--666 (2001; Zbl 1017.62022) Full Text: DOI