PPT-Another look at Bayesian inference

A general scenario Query variables X Evidence observed variables and their values E e Unobserved variables Y Inference problem answer questions about the query variables given the evidence variables. De64257nition A Bayesian nonparametric model is a Bayesian model on an in64257nitedimensional parameter space The parameter space is typically chosen as the set of all possi ble solutions for a given learning problem For example in a regression prob Read R&N Ch. 14.1-14.2. Next lecture: Read R&N 18.1-18.4. You will be expected to know. Basic concepts and vocabulary of Bayesian networks.. Nodes represent random variables.. Directed arcs represent (informally) direct influences.. Author: David Heckerman. . Presented By:. Yan Zhang - 2006. Jeremy Gould – 2013. 1. Outline. Bayesian Approach. Bayesian vs. classical probability methods. Examples. Bayesian Network. Structure. Department of Electrical and Computer Engineering. Zhu Han. Department. of Electrical and Computer Engineering. University of Houston.. Thanks to Nam Nguyen. , . Guanbo. . Zheng. , and Dr. . Rong. . Author: David Heckerman. . Presented By:. Yan Zhang - 2006. Jeremy Gould – 2013. 1. Outline. Bayesian Approach. Bayesian vs. classical probability methods. Examples. Bayesian Network. Structure. Kathryn Blackmond Laskey. Department of Systems Engineering and Operations Research. George Mason University. Dagstuhl. Seminar April 2011. The problem of plan recognition is to take as input a sequence of actions performed by an actor and to infer the goal pursued by the actor and also to organize the action sequence in terms of a plan structure. A general scenario:. Query . variables:. . X. Evidence . (. observed. ) . variables and their values: . E. = . e. . Unobserved . variables: . Y. . Inference problem. : answer questions about the query variables given the evidence variables. Henrik Singmann. A girl had NOT had sexual intercourse.. How likely is it that the girl is NOT pregnant?. A girl is NOT pregnant. . How likely is it that the girl had NOT had sexual intercourse?. A girl is pregnant. . or. How to combine data, evidence, opinion and guesstimates to make decisions. Information Technology. Professor Ann Nicholson. Faculty of Information Technology. Monash University . (Melbourne, Australia). (BO). Javad. . Azimi. Fall 2010. http://web.engr.oregonstate.edu/~azimi/. Outline. Formal Definition. Application. Bayesian Optimization Steps. Surrogate Function(Gaussian Process). Acquisition Function. Problem statement. Objective is to estimate or infer unknown parameter . q . based on observations y. Result is given by probability distribution.. Identify parameter . q . that we’d like to estimate.. Mathys. Wellcome Trust Centre for Neuroimaging. UCL. London SPM Course. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. Robert J. . Tempelman. Department of Animal Science. Michigan State University. 1. Outline of talk:. Introduction. Review . of Likelihood Inference . An Introduction to Bayesian Inference. Empirical Bayes Inference. Christopher M. Bishop. Microsoft Research, Cambridge. Microsoft Research Summer School 2009. First Generation. “Artificial Intelligence” (GOFAI). Within a generation ... the problem of creating ‘artificial intelligence’ will largely be solved.