PPT-Probabilistic Latent-Factor Database Models

Author : phoebe-click | Published Date : 2017-06-28

Denis Krompaß 1 Xueyan Jiang 1 Maximilian Nickel 2 and Volker Tresp 13 1 Department of Computer Science Ludwig Maximilian 2 University Oettingenstraße 67 80538

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Probabilistic Latent-Factor Database Models: Transcript

Denis Krompaß 1 Xueyan Jiang 1 Maximilian Nickel 2 and Volker Tresp 13 1 Department of Computer Science Ludwig Maximilian 2 University Oettingenstraße 67 80538 Munich Germany. Latent Classes. A population contains a mixture of individuals of different types (classes). Common form of the data generating mechanism within the classes. Observed outcome y is governed by the . common process . Peter Congdon, Queen Mary University of London, School of Geography & Life Sciences Institute. Outline. Background. Bayesian approaches: advantages/cautions. Bayesian Computing, Illustrative . BUGS model, Normal Linear . Part II: Definition and Properties. Nevin. L. Zhang. Dept. of Computer Science & Engineering. The Hong Kong Univ. of Sci. & Tech.. http://www.cse.ust.hk/~lzhang. AAAI 2014 Tutorial. Part II: Concept . Alexander Kotov. 1. , . Mehedi. Hasan. 1. , . April . Carcone. 1. , Ming Dong. 1. , Sylvie Naar-King. 1. , Kathryn Brogan Hartlieb. 2. . 1 . Wayne State University. 2 . Florida International University. effect models. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Dec . Machine Learning @ CU. Intro courses. CSCI 5622: Machine Learning. CSCI 5352: Network Analysis and Modeling. CSCI 7222: Probabilistic Models. Other courses. cs.colorado.edu/~mozer/Teaching/Machine_Learning_Courses. Latent Growth Models. Brad Verhulst & . Lindon. Eaves. Two Broad Categories of Developmental Models. Autoregressive Models: . The things that happened yesterday affect what happens today, which affect what happens tomorrow. Nevin. L. Zhang. Dept. of Computer Science & Engineering. The Hong Kong Univ. of Sci. & Tech.. http://www.cse.ust.hk/~lzhang. AAAI 2014 Tutorial. HKUST. 2014. HKUST. 1988. Latent Tree Models. Phillip . Wood, Wolfgang . Wiedermann. , . Douglas . Steinley. University of Missouri. Some Questions We Wish We Could Answer with Longitudinal Data. Are there Different Types of Learners? . Slow Versus Quick. Latent Classes. A population contains a mixture of individuals of different types (classes). Common form of the data generating mechanism within the classes. Observed outcome y is governed by the . common process . Trang Quynh Nguyen, May 9, 2016. 410.686.01 Advanced Quantitative Methods in the Social and Behavioral Sciences: A Practical Introduction. Objectives. Provide a QUICK introduction to latent class models and finite mixture modeling, with examples. Peter Congdon, Queen Mary University of London, School of Geography & Life Sciences Institute. Outline. Background. Bayesian approaches: advantages/cautions. Bayesian Computing, Illustrative . BUGS model, Normal Linear . Nevin. L. Zhang. Dept. of Computer Science & Engineering. The Hong Kong Univ. of Sci. & Tech.. http://www.cse.ust.hk/~lzhang. AAAI 2014 Tutorial. Part II: Concept . and Properties. Latent . Tree . Nisheeth. Coin toss example. Say you toss a coin N times. You want to figure out its bias. Bayesian approach. Find the generative model. Each toss ~ Bern(. θ. ). θ. ~ Beta(. α. ,. β. ). Draw the generative model in plate notation.

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