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. (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. 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. 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. 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. Shou-pon. Lin. Advisor: Nicholas F. . Maxemchuk. Department. . of. . Electrical. . Engineering,. . Columbia. . University,. . New. . York,. . NY. . 10027. . Problem: . Markov decision process or Markov chain with exceedingly large state space. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. Warm up. Share your picture with the people at your table group.. Make sure you have your Science notebook, agenda and a sharpened pencil. use tape to put it in front of your table of contents. Describe the difference between observations and inferences. 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 . Machine Learning @ CU. Intro courses. CSCI 5622: Machine Learning. CSCI 5352: Network Analysis and Modeling. CSCI 7222: Probabilistic Models. Other courses. cs.colorado.edu/~mozer/Teaching/Machine_Learning_Courses. . – 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. 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. , . Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . Chapter 5: Probabilistic Query Answering (3). 2. Objectives. In this chapter, you will:. Learn the definition and query processing techniques of a probabilistic query type. Probabilistic Reverse Nearest Neighbor Query.