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通过变分自编码器快速灵活地模拟空间极值过程AI 解读

Many real-world processes have complex tail dependence structures that cannot be adequately characterized using classical Gaussian processes. Alternatively, models motivated by extreme-value theory exhibit appealing extremal dependence properties but are often exceedingly prohibitive to fit and simulate from in high dimensions using classical methods. In this paper, we extend the boundaries on computation and modeling of high-dimensional spatial extremes by integrating a new…

具有共轭满条件分布的自然指数族相关数据贝叶斯层级模型AI 解读

We introduce a Bayesian approach for analyzing (possibly) high-dimensional dependent data that are distributed according to a member from the natural exponential family of distributions. This problem requires extensive methodological advancements, as jointly modeling high-dimensional dependent data leads to the so-called “big n problem.” The computational complexity of the “big n problem” is further exacerbated when allowing for non-Gaussian data models, as is the case here.…

高维层级动态时空模型的集合卡尔曼方法AI 解读

We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combine the ensemble Kalman filter and smoother, developed in the geophysics literature, with state-space algorithms from the statistics literature. Our algorithms address a variety of estimation scenarios, including online and off-line state and parameter estimation. We take a Bayesian perspective, for…

计数型调查数据的贝叶斯空间支撑转换：美国社区调查应用AI 解读

Jonathan R. Bradley*, Christopher K. Wikle & Scott H. HolanCONTACT Jonathan R. Bradley bradleyjr@missouri.edu Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211-6100Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.ABSTRACTWe introduce Bayesian spatial change of support (COS) methodology for count-valued survey data with known survey variances. Our proposed methodology is…

离散时空系统的统计主体模型AI 解读

Agent-based models have been used to mimic natural processes in a variety of fields, from biology to social science. By specifying mechanistic models that describe how small-scale processes function and then scaling them up, agent-based approaches can result in very complicated large-scale behavior while often relying on only a small set of initial conditions and intuitive rules. Although many agent-based models are used strictly in a simulation context, statistical…