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费城犯罪趋势：结合粒子优化的贝叶斯聚类AI 解读

Accurate estimation of the change in crime over time is a critical first step toward better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled "sharing of information" between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial…

HIV对治疗进化响应的贝叶斯层级建模AI 解读

A major challenge for the treatment of human immunodeficiency virus (HIV) infection is the development of therapy-resistant strains. We present a statistical model that quantifies the evolution of HIV populations when exposed to particular therapies. A hierarchical Bayesian approach is used to estimate differences in rates of nucleotide changes between treatment- and control-group sequences. Each group's rates are allowed to vary spatially along the HIV genome. We employ a…

考虑时间序列依赖的统计学习：小鼠睡眠评分应用AI 解读

We develop methodology which combines statistical learning methods with generalized Markov models, thereby enhancing the former to account for time series dependence. Our methodology can accommodate very general and very long-term time dependence structures in an easily estimable and computationally tractable fashion. We apply our methodology to the scoring of sleep behavior in mice. As currently used methods are expensive, invasive, and labor intensive, there is…

收入波动异质性的半参数贝叶斯建模AI 解读

Research on income risk typically treats its proxy—income volatility, the expected magnitude of income changes—as if it were unchanged for an individual over time, the same for everyone at a point in time, or both. In reality, income risk evolves over time, and some people face more of it than others. To model heterogeneity and dynamics in (unobserved) income volatility, we develop a novel semiparametric Bayesian stochastic volatility model. Our Markovian hierarchical…