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连接两类蒙特卡洛采样器的折线路径：分段确定性马尔可夫过程的哈密顿对应AI 解读

Zigzag and other piecewise deterministic Markov process samplers have attracted significant interest for their non-reversibility and other appealing properties for Bayesian posterior computation. Hamiltonian Monte Carlo is another state-of-the-art sampler, exploiting fictitious momentum to guide Markov chains through complex target distributions. We establish an important connection between the zigzag sampler and a variant of Hamiltonian Monte Carlo based on…

以先验预条件共轭梯度法加速大样本高维贝叶斯稀疏回归的 Gibbs 抽样AI 解读

In a modern observational study based on healthcare databases, the number of observations and of predictors typically range in the order of 10^{5}-10^{6} and of 10^{4}-10^{5}. Despite the large sample size, data rarely provide sufficient information to reliably estimate such a large number of parameters. Sparse regression techniques provide potential solutions, one notable approach being the Bayesian method based on shrinkage priors. In the "large n and large p" setting,…

在大量测量不完整的大型系统发育树上推断表型性状演化AI 解读

Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing…

正则化系统发育推断的一致性与收敛速度AI 解读

It is common in phylogenetics to have some, perhaps partial, information about the overall evolutionary tree of a group of organisms and wish to find an evolutionary tree of a specific gene for those organisms. There may not be enough information in the gene sequences alone to accurately reconstruct the correct "gene tree." Although the gene tree may deviate from the "species tree" due to a variety of genetic processes, in the absence of evidence to the contrary it is…