PPT-Basic Parametric Bayes Models

Basic Representation of Parametric Bayesian Statistics Data model likelihood Prior beliefs about parameters Posterior beliefs about parameters Sometimes you get posteriors that are the same form as the priors . Michael I. . Jordan. INRIA. University of California, Berkeley. Acknowledgments. : . Brian . Kulis. , Tamara . Broderick. May 11, 2013. Statistical Inference and Big Data. Two major needs: models with open-ended complexity and scalable algorithms that allow those models to be fit to data. Graphcial. Causal Models. Richard . Scheines. Joe Ramsey. Carnegie Mellon University. Peter Spirtes, Clark Glymour. Goals. Convey rudiments of graphical causal models. Basic working knowledge of Tetrad IV. Bayes. Approach to Sample Survey Inference. Roderick . Little. Department of Biostatistics, University of Michigan. Associate Director for Research & Methodology, Bureau of Census. Learning Objectives. for beginners. Methods for . dummies. 27 February 2013. Claire Berna. Lieke de Boer. Bayes . rule. Given . marginal probabilities . p(A. ), p(B. ), . and . the . joint probability p(A,B. ), . we can . T.M-L. Andersson. 1. ,. S. Eloranta. 1. ,. P.W. Dickman. 1. , . P.C. Lambert. 1,2. 1. Medical . Epidemiology. and . Biostatistics. , Karolinska Institutet, Stockholm, Sweden. 2 . Department of Health Sciences, University of Leicester, UK. . Sponsered. Workshop:. Nonparametric Tests. Heather . Hulton. . VanTassel. 2.27.2014. Workshop Outline. Workshop Goal. To be equipped with the basic skills of how to analyze nonparametric data! . adjustment . framework of JDemetra+. Jean.palate@nbb.be. CESS 2016. Budapest. 0. Outline. Overview of the main SA methods . Design. SA framework: common features. Extensions. Next challenges. 1. SA . Graphcial. Causal Models. Richard . Scheines. Joe Ramsey. Carnegie Mellon University. Peter Spirtes, Clark Glymour. Goals. Convey rudiments of graphical causal models. Basic working knowledge of Tetrad IV. 1. Semi-Supervised Learning. Can we improve the quality of our learning by combining labeled and unlabeled data. Usually a lot more unlabeled data available than labeled. Assume a set . L. of labeled data and . Volkan. Cevher. Laboratory. for Information and Inference Systems (LIONS). École. . Polytechnique. . Fédérale. de Lausanne (EPFL). Switzerland . http://lions.epfl.ch . . joint work with . Hemant. Generative vs. Discriminative models. Christopher Manning. Introduction. So far we’ve looked at “generative models”. Language models, Naive Bayes. But there is now much use of conditional or discriminative probabilistic models in NLP, Speech, IR (and ML generally). Human and Machine Learning. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Today’s Plan. Hand back Assignment 1. More fun stuff from motion perception model. * Figures are from the . textbook site. .. II. Naïve Bayes model. III. Revisiting the . wumpus. world. I. Combining Evidence. What happens when we have two or more pieces of evidences?. . Suppose we know the full joint distribution.. T.M-L. Andersson. 1. ,. S. Eloranta. 1. ,. P.W. Dickman. 1. , . P.C. Lambert. 1,2. 1. Medical . Epidemiology. and . Biostatistics. , Karolinska Institutet, Stockholm, Sweden. 2 . Department of Health Sciences, University of Leicester, UK.