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Evolutionary Monte Carlo: Applications to C_p Model Sampling and Change Point Problem
 STATISTICA SINICA
, 2000
"... Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm so called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms ..."
Abstract

Cited by 25 (5 self)
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Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm so called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms and simulated annealing into the framework of MCMC. It works by simulating a population of Markov chains in parallel, where each chain is attached to a different temperature. The population is updated by mutation (Metropolis update), crossover (partial state swapping) and exchange operators (full state swapping). The algorithm is illustrated through examples of the Cpbased model selection and changepoint identification. The numerical results and the extensive comparisons show that evolutionary Monte Carlo is a promising approach for simulation and optimization.
THE PREDICTOR'S AVERAGE ESTIMATED VARIANCE CRITERION FOR THE SELECTIONOFVARIABLES PROBLEM IN GENERAL LINEAR MODELS By
, 1971
"... 1. The matrix XIX should be replaced by 1. XIX n (a) in lines 3, 4 and 12 on p. 17. (b) in line 7, p. 22. (c) in line 2, p. 25. 2. The expression of AEV(y) = s2p. 2 should read AEV(y) = s pIn on line 13, p. 17, and AEV(Yi) = si2p/n on line 2, p. 25. 3. In Table 2 (pp. 2324) the calculated AEV st ..."
Abstract
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1. The matrix XIX should be replaced by 1. XIX n (a) in lines 3, 4 and 12 on p. 17. (b) in line 7, p. 22. (c) in line 2, p. 25. 2. The expression of AEV(y) = s2p. 2 should read AEV(y) = s pIn on line 13, p. 17, and AEV(Yi) = si2p/n on line 2, p. 25. 3. In Table 2 (pp. 2324) the calculated AEV statistics should be divided by 68.I I