PPT-BAYESIAN INFERENCE “The theory of probabilities is basically only common sense

reduced to calculus PS Laplace See Lecture Notes Chapter 2 at arXiv161005590v3 examples exercises and references Lecture 3 STATISTICS 1 To ask the right question is harder than to answer it. 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 Bayesian Network Motivation. We want a representation and reasoning system that is based on conditional . independence. Compact yet expressive representation. Efficient reasoning procedures. Bayesian Networks are such a representation. 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.. 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. 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. Recall the hidden Markov model (HMM). a finite state automata with nodes that represent hidden states (that is, things we cannot necessarily observe, but must infer from data) and two sets of links. transition – probability that this state will follow from the previous state. Recall the hidden Markov model (HMM). a finite state automata with nodes that represent hidden states (that is, things we cannot necessarily observe, but must infer from data) and two sets of links. transition – probability that this state will follow from the previous state. 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. SPM Course. London, May 12, 2014. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. 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. Introduction to Computer Vision. Xiao Lin. 2/10/2016. 2. What’s in the images?. Man. Bowl. Popcorn. Sofa. P. illows. Sweater. Jeans. Women. Magazine. Books. Bookshelf. Desk. Handbags. Hat. Drawing. La gamme de thé MORPHEE vise toute générations recherchant le sommeil paisible tant désiré et non procuré par tout types de médicaments. Essentiellement composé de feuille de morphine, ce thé vous assurera d’un rétablissement digne d’un voyage sur . Topic 1.c. Paper – 1 . Sociology and Common Sense. Need to ponder. If sociology is study of obvious or application of common sense? . But common sense lacks validity and presents itself as a mere assertion. Outline. I. Semantics. * Figures are either from the . textbook site. or by the instructor.. II. Network construction. III. Conditional independence relations. I. Knowledge in an Uncertain Domain. .