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作者：Diana, Alex; Matechou, Eleni; Griffin, Jim; Johnston, Alison
作者单位：University of Kent; University of London; University College London; Cornell University
摘要：Environmental changes in recent years have been linked to phenological shifts which in turn are linked to the survival of species. The work in this paper is motivated by capture-recapture data on blackcaps collected by the British Trust for Ornithology as part of the Constant Effort Sites monitoring scheme. Blackcaps overwinter abroad and migrate to the UK annually for breeding purposes. We propose a novel Bayesian nonparametric approach for expressing the bivariate density of individual arriv...
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作者：Shi, Peng; Zhao, Zifeng
作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Notre Dame
摘要：In actuarial research a task of particular interest and importance is to predict the loss cost for individual risks so that informative decisions are made in various insurance operations such as underwriting, ratemaking and capital management. The loss cost is typically viewed to follow a compound distribution where the summation of the severity variables is stopped by the frequency variable. A challenging issue in modeling such outcomes is to accommodate the potential dependence between the n...
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作者：Kunihama, Tsuyoshi; Li, Zehang Richard; Clark, Samuel J.; McCormick, Tyler H.
作者单位：Kwansei Gakuin University; Yale University; University System of Ohio; Ohio State University; University of Washington; University of Washington Seattle
摘要：The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions o...
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作者：Ruiz, Francisco J. R.; Athey, Susan; Blei, David M.
作者单位：University of Cambridge; Stanford University; Columbia University
摘要：We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in particular, we designed SHOPPER to capture how items interact with other items. We develop an efficient posterior inference algorithm to estimate these forces from large-scale data, and we analyze a large dataset from a major chain grocery store. We are interested in answering counterfactual queries about changes in pric...
