Adaptive Computational Methods for Bayesian Variable Selection
Handbook of Bayesian Variable Selection(2021)
摘要
Efficient computational methods to sample from the posterior distribution of models are key to the use of Bayesian variable selection in practice. Adaptive Monte Carlo are a promising approach to build such methods. We review the use of these methods in generalised linear models with a particular focus on linear and logistic regression. We illustrate how these methods can be applied to simulated data and to two contrasting real-life examples. Firstly, we consider a high-dimensional example with an application to fine mapping in genomics with 10,995 observations and 5766 covariates (SNPs). Secondly, we consider an application to a complex model of environmental DNA which contains five logistic regressions and two sets of latent variables.
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关键词
bayesian,selection,computational,methods,variable
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