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关于“非参数经验贝叶斯分析的置信区间”的讨论AI 解读

We congratulate Ignatiadis and Wager (IW) on this significant step forward, incorporating uncertainty in the estimated prior into empirical Bayes (EB) analysis. EB analyses have traditionally ignor...

回归变量选择的新方法及其在遗传精细定位中的应用AI 解读

Summary We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model—the ‘sum of single effects’ model, called ‘SuSiE’—which comes from writing the sparse vector of regression coefficients as a sum of ‘single-effect’ vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure—iterative Bayesian…

分子群体遗传学中的统计推断AI 解读

Summary Full likelihood-based inference for modern population genetics data presents methodological and computational challenges. The problem is of considerable practical importance and has attracted recent attention, with the development of algorithms based on importance sampling (IS) and Markov chain Monte Carlo (MCMC) sampling. Here we introduce a new IS algorithm. The optimal proposal distribution for these problems can be characterized, and we exploit a detailed…

混合模型中标签交换问题的处理AI 解读

Summary In a Bayesian analysis of finite mixture models, parameter estimation and clustering are sometimes less straightforward than might be expected. In particular, the common practice of estimating parameters by their posterior mean, and summarizing joint posterior distributions by marginal distributions, often leads to nonsensical answers. This is due to the so-called ‘label switching’ problem, which is caused by symmetry in the likelihood of the model parameters. A…

成分数未知的混合模型贝叶斯分析：可逆跳跃方法的替代方案AI 解读

Richardson and Green present a method of performing a Bayesian analysis of data from a finite mixture distribution with an unknown number of components. Their method is a Markov Chain Monte Carlo (MCMC) approach, which makes use of the “reversible jump” methodology described by Green. We describe an alternative MCMC method which views the parameters of the model as a (marked) point process, extending methods suggested by Ripley to create a Markov birth-death process with an…