Efficient Bayesian Nonparametric Modelling of Structured Point Processes
Tom Gunter, Chris Lloyd, Michael A. Osborne, Stephen J. Roberts
备注：
Presented at UAI 2014. Bibtex: @inproceedings{structcoxpp14_UAI, Author = {Tom Gunter and Chris Lloyd and Michael A. Osborne and Stephen J. Roberts}, Title = {Efficient Bayesian Nonparametric Modelling of Structured Point Processes}, Booktitle = {Uncertainty in Artificial Intelligence (UAI)}, Year = {2014}}
发表信息：
Proceedings of Uncertainty in Artificial Intelligence (UAI) 2014
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The Australian National University（澳大利亚国立大学）；Data61/NICTA；Monash University（莫纳什大学）；Duke University（杜克大学）
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Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
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The University of Sheffield（谢菲尔德大学）；Laboratory for Verification and Validation（验证与确认实验室）
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TU Wien（维也纳工业大学）；Boston University（波士顿大学）；University of Guelph（圭尔夫大学）；Vector Institute（矢量研究所）；University of Texas, Austin（德克萨斯大学奥斯汀分校）
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Indian Institute of Technology, Kanpur（印度理工学院坎普尔分校）
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University of Manchester（曼彻斯特大学）；University of Sheffield（谢菲尔德大学）
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Layer 6 AI（Layer 6人工智能公司）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）
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University of Sheffield（谢菲尔德大学）；University of Bristol（布里斯托大学）
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TU Darmstadt（达姆施塔特工业大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Aalto University（阿尔托大学）；Centre for Cognitive Science TU Darmstadt（达姆施塔特工业大学认知科学中心）；Hessian Center for AI (hessian.AI)（黑森人工智能中心（hessian.AI））
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University of Cambridge（剑桥大学）；The Alan Turing Institute（艾伦·图灵研究所）；University of California Los Angeles（加州大学洛杉矶分校）
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University of Sheffield（谢菲尔德大学）；University of Manchester（曼彻斯特大学）
In this publication, we combine two Bayesian non-parametric models: the Gaussian Process (GP) and the Dirichlet Process (DP). Our innovation in the GP model is to introduce a variation on the GP prior which enables us to model structured time-series data, i.e. data containing groups where we wish to model inter- and intra-group variability. Our innovation in the DP model is an implementation of a new fast collapsed variational inference procedure which enables us to optimize our variationala pproximation significantly faster than standard VB approaches. In a biological time series application …
Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics. However, existing methods for parameter estimation, inference, and structure learning mainly rely on the Gaussian or discrete assumptions, which are restrictive under many applications. In this paper, we propose new nonparametric methods based on reproducing kernel Hilbert space embeddings of distributions that can recover the latent tree structures, estimate the parameters, and perform infere…
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Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology（数学系，先进科学学院，维洛雷理工学院）；Applied Statistics Unit, Indian Statistical Institute（应用统计单位，印度统计研究所）
Columbia University（哥伦比亚大学）；Adobe Research（Adobe研究院）；Adobe Systems Incorporated（奥多比系统公司）
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Centre for Translational Data Science（转化数据科学中心）；University of Sydney（悉尼大学）；University of Sydney Business School（悉尼大学商学院）
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