Individual adaptation: an adaptive MCMC scheme for variable selection\n problems

2014/12/21 by Jim E. Griffin, Griffin, Jim, Krzysztof Łatuszyński +3
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning and Algorithms #Markov Chains and Monte Carlo Methods #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1412.6760

openalex publication_date 2014/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The increasing size of data sets has lead to variable selection in regression\nbecoming increasingly important. Bayesian approaches are attractive since they\nallow uncertainty about the choice of variables to be formally included in the\nanalysis. The application of fully Bayesian variable selection methods to large\ndata sets is computationally challenging. We describe an adaptive Markov chain\nMonte Carlo approach called Individual Adaptation which adjusts a general\nproposal to the data. We show that the algorithm is ergodic and discuss its use\nwithin parallel tempering and sequential Monte Carlo approaches. We illustrate\nthe use of the method on two data sets including a gene expression analysis\nwith 22 577 variables.\n

Citations

• An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration

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