PPT-Using Markov Blankets for Causal Structure Learning

JeanPhilippe Pellet Andre Ellisseeff Presented by Na Dai Motivation Why structure l earning What are Markov blankets Relationship between feature selection and Markov blankets Previous work. T state 8712X action or input 8712U uncertainty or disturbance 8712W dynamics functions XUW8594X w w are independent RVs variation state dependent input space 8712U 8838U is set of allowed actions in state at time brPage 5br Policy action is function Causes. : . Reconciling Competing Theories of Causal Reasoning.   . Michael R. . Waldmann . Cognitive . and Decision Sciences. Department of Psychology . University of Göttingen. With: Ralf . Mayrhofer. Motivation. Representing/Modeling Causal Systems. Estimation and Updating. Model Search. Linear . Latent Variable . Models. Case Study: . fMRI. 1. Outline. Search I: Causal Bayes Nets. Bridge Principles: . Network. . Ben . Taskar. ,. . Carlos . Guestrin. Daphne . Koller. 2004. Topics Covered. Main Idea.. Problem Setting.. Structure in classification problems.. Markov Model.. SVM. Combining SVM and Markov Network.. Elena Popa. Children’s causal learning and evidence.. Causation, intervention, and Bayes nets.. The conditional intervention principle and Woodward’s concept of an intervention.. Conclusions: Connecting causality and evidence with intervention – causal learning.. (Markov Nets). (Slides from Sam . Roweis. ). Connection to MCMC:. . . MCMC requires sampling a node given its . markov. blanket. . Need to use P(. x|MB. (x)). . . For . Bayes. nets MB(x) contains more. Liu . ze. . yuan. May 15,2011. What purpose does . Markov Chain Monte-Carlo(MCMC) . serve in this chapter?. Quiz of the Chapter. 1 Introduction. 1.1Keywords. 1.2 Examples. 1.3 Structure discovery problem. A Service Project. By: Grace Fassio, Lexa Wendl, Via Cooper, & Emily Carbon. Overview. Fundraising for Blanket Supplies. Bake Sale. Shop for Supplies . Make Blankets. Deliver Blankets. Planning The Bake Sale. Model Definition. Comparison to Bayes Nets. Inference techniques. Learning Techniques. A. B. C. D. Qn. : What is the. . most likely. . configuration of A&B?. Factor says a=b=0. But, marginal says. Perceptron. SPLODD. ~= AE* – 3, 2011. * Autumnal Equinox. Review. Computer science is full of . equivalences. SQL .  relational algebra. YFCL optimizing … on the training data. g. cc. –O4 . Austin Nichols (Abt) & Linden McBride (Cornell). July 27, 2017. Stata Conference. Baltimore, MD. Overview. Machine learning methods dominant for classification/prediction problems.. Prediction is useful for causal inference if one is trying to predict propensity scores (probability of treatment conditional on observables);. Gordon Hazen. February 2012. Medical Markov Modeling. We think of Markov chain models as the province of operations research analysts. However …. The number of publications in medical journals . using Markov models. Oliver Schulte. Zhensong. Qian. Arthur. Kirkpatrick. Xiaoqian. . Yin. Yan. Sun. Relational Dependency Networks. Neville, J. & Jensen, D. (2007), 'Relational Dependency Networks', . Journal of Machine Learning Research . Cognitive Science. Current Problem:. . How do children learn and how do they get it right?. Connectionists and Associationists. Associationism:. . maintains that all knowledge is represented in terms of associations between ideas, that complex ideas are built up from combinations of more primitive ideas, which, in accordance with empiricist philosophy, are ultimately derived from the senses. .