PPT-Probabilistic Inference Agenda

Random variables Bayes rule Intro to Bayesian Networks Cheat Sheet Probability Distributions Event boolean propositions that may or may not be true in a state of the world For any event x. However subjects make discrete responses and report the phenomenal contents of their mind to be allornone states rather than graded probabilities How can these 2 positions be reconciled Selective attention tasks such as those used to study crowding in human semantic memory. Mark . Steyvers. , Tomas L. Griffiths, and Simon Dennis. 소프트컴퓨팅연구실. 오근현. TRENDS in Cognitive Sciences vol. . 10, . no. . 7, 2006. Overview . Relational models of memory. Michael Hicks. Piotr (Peter) Mardziel. University of Maryland, College Park. Stephen Magill. Galois. Michael Hicks. UMD. Mudhakar. . Srivatsa. IBM TJ Watson. Jonathan Katz. UMD. Mário. . Alvim. UFMG. Thesis Defense, 7/29/2011. Jonathan Huang. Collaborators:. Carlos . Guestrin. CMU. Leonidas. . Guibas. Stanford. Xiaoye. Jiang. Stanford. Ashish. . Kapoor. Microsoft. Political Elections in Ireland. (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. How the Quest for the Ultimate Learning Machine Will Remake Our World. Pedro Domingos. University of Washington. Machine Learning. Traditional Programming. Machine Learning. Computer. Data. Algorithm. 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 Probabilistic . Lexical . Inference System. . Eyal Shnarch. ,. . Ido . Dagan, Jacob . Goldberger. PLIS - Probabilistic Lexical Inference System. 1. /34. The . entire talk in a single sentence. Taisuke. Sato. Tokyo Institute of Technology. Problem. model-specific learning algorithms. Model 1. EM. VB. MCMC. Model 2. Model n. .... .... EM. 1. EM. 2. EM. n. Statistical machine learning is a . Thinking and Everyday Life. Michael K. Tanenhaus. Inference in an uncertain world. Most of what we do, whether consciously or unconsciously involves probabilistic inference. Decisions. Some are conscious:. Learning Revealed. . Pedro Domingos. . University of Washington. Where Does Knowledge Come From?. Evolution. Experience. Culture. Where Does Knowledge Come From?. Evolution. Experience. Culture. Computers. . – What Next?. Martin Theobald. University of Antwerp. Joint work with . Maximilian Dylla, Sairam Gurajada, Angelika . Kimmig. , Andre . Melo. , Iris Miliaraki, . Luc de . Raedt. , Mauro . Sozio. Thesis Defense, 7/29/2011. Jonathan Huang. Collaborators:. Carlos . Guestrin. CMU. Leonidas. . Guibas. Stanford. Xiaoye. Jiang. Stanford. Ashish. . Kapoor. Microsoft. Political Elections in Ireland. Rodrigo de Salvo Braz. Ciaran O’Reilly. Artificial Intelligence Center - SRI International. Vibhav Gogate. University of Texas at Dallas. Rina Dechter. University of California, Irvine. IJCAI-16. , .