PPT-Bayesian Decision Theory

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. Chris . Mathys. Wellcome Trust Centre for Neuroimaging. UCL. SPM Course (M/EEG). London, May 14, 2013. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. of medical . decision-. making. . 1. :. . The . CREDO stack. John Fox . Department of Engineering Science . University of . Oxford . and OpenClinical. Thanks to …. Psychologists, Informatics/CS/AI. . decisions. in . conditions. . of. . information. . uncertainty. Tomas . Macak. MANAGEMENT THEORY. Faculty of Economics and Management. Department of Management. Organization of teaching. Consultation time: Monday from 10.00 to 11.30 in the morning. Office No. E 468 (4th floor of the building of the Faculty of Business and Economics). Phone: 224 382 029, email: macak@pef.czu.cz. Case . Studies. CS479/679 Pattern Recognition. Dr. George . Bebis. Case Study I. A. . Madabhushi. and J. . Aggarwal. , . A . bayesian. approach to human activity recognition. , 2nd International Workshop on Visual Surveillance, pp. 25-30, June 1999. . ICM. , Paris, . France. ETH, Zurich, Switzerland. Dynamic. Causal . Modelling. of . fMRI. . timeseries. . Overview. 1 DCM: introduction. 2 Dynamical systems theory. 4 Bayesian inference. . 5 Conclusion. Example decision: End point planning Example decision: Random Dot Coherent motion paradigm Example decision: Random Dot Coherent motion paradigm Example decision: Random Dot Coherent motion paradigm W Abby . Yinger. Mathematics. Statistics. Decision Theory. Decision theory. The Decision . theory . is the theory . about decisions. The subject is not a very unified one. T. here . are many different ways to theorize about decisions, . Janet G. Lenz, Ph.D.. Gary W. Peterson, Ph.D.. Robert C. Reardon, Ph.D.. James . P. Sampson, Jr., Ph.D.. Denise E. Saunders, Ph.D.. Evolution of CIP. Innovation in . brief. and . self-help services . 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). is the empirical evidence for Bayesian models of cognition?. Gary Marcus & Ernie Davis. “Is the Brain Bayesian?” Workshop. December 4, 2015. A strong view. “People reason in ways that are consistent with optimal Bayesian models in a variety of tasks”. Denison et al. (2009).. It has 4 phases.. 1.Action or acts.. 2.State of nature or events or outcome.. 3.Pay off and pay off table or pay off matrix.. Decision. A decision problem may be represented by tree diagram. Decision making problems deals with the selection of single act from a set of acts.. and . RATIONALITY – Some general comments. 2. 3. Decision Theory. Formidable foundations. Probability and reasoning about the future. Rational decision making. Deeply rooted in the Enlightenment. Major leaps in the mid-20. Neil Bramley. Intro. 1. Limitations of Causal . Bayes. Nets as psychological models.. 2. Extension of the approach using the hierarchical Bayesian framework.. 3. Philosophical implications of this framework. 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.