PPT-Bayesian Classification

Week 9 and Week 10 1 Announcement Midterm II 415 Scope Data warehousing and data cube Neural network Open book Project progress report 422 2 Team Homework Assignment 11 Read pp 311 314. 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 . Rebecca R. Gray, Ph.D.. Department of Pathology. University of Florida. BEAST:. is a cross-platform program for Bayesian MCMC analysis of molecular sequences. entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. P(. A . &. B. ) . = . P(. A. |. B. ) * P(. B. ). Product Rule:. Bayesian Reasoning. P(. A . &. B. ) . = . P(. A. |. B. ) * P(. B. ). Product Rule:. Shorthand for . . P(A=true & B=true) = P(A=true | B=true) * P(B=true). 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. Misstear. Spam Filtering. An Artificial Intelligence Showcase. What is Spam. Messages sent indiscriminately to a large number of recipients. We all hate it. Term attributed to a Monty Python skit. Legitimate messages sometimes referred to as “ham. Alex Yakubovich. Elderlab. Oct 7, 2011. John Wilder, Jacob Feldman, Manish Singh, . Superordinate shape classification using natural shape statistics. , Cognition, Volume 119, Issue 3, June 2011, Pages 325-340. Abel Sanchez, John R Williams. Stunningly Simple. The . mathematics . of Bayes Theorem are . stunningly simple. In its most basic form, it is just an . equation . with three known variables and one unknown one. . (BO). Javad. . Azimi. Fall 2010. http://web.engr.oregonstate.edu/~azimi/. Outline. Formal Definition. Application. Bayesian Optimization Steps. Surrogate Function(Gaussian Process). Acquisition Function. Inference implemented on . FPGA. with . Stochastic . Bitstreams. for an Autonomous Robot . Jorge Lobo. jlobo@isr.uc.pt. Bayesian Inference implemented on FPGA. with Stochastic . Bitstreams. for an Autonomous Robot . Making Decisions Under uncertainty. 1. Overview. Basics of Probability and the Bayes Rule. Bayesian . Classification. Losses and . Risks. Discriminant Function. Utility Theory. Association . Rule Learning. Javad. . Azimi. Fall 2010. http://web.engr.oregonstate.edu/~azimi/. Outline. Formal Definition. Application. Bayesian Optimization Steps. Surrogate Function(Gaussian Process). Acquisition Function. PMAX. Sjors . H.W. Scheres. EMBO course . 2019. Birkbeck. College, London. Agenda. An intuitive introduction. Alignment. Dealing with the incomplete problem. maxCC. . vs. ML (real-space). Classification. Jingjing Ye, PhD. BeiGene. PSI Journal Club: Bayesian Methods. Nov. 17, 2020. Outline. Background . Using a case study to illustrate potential useful Bayesian analysis. Analysis and monitoring. Design study.