PPT-Markov Logic: Combining

Logic and Probability Parag Singla Dept of Computer Science amp Engineering Indian Institute of Technology Delhi Overview Motivation amp Background Markov logic Inference amp Learning Abductive. The fundamental condition required is that for each pair of states ij the longrun rate at which the chain makes a transition from state to state equals the longrun rate at which the chain makes a transition from state to state ij ji 11 Twosided stat 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 Please do not alter or modify contents All rights reserved QVSIBTFE 1BJOMTT1BSOUJOHSUI1STDIMBST BDLTPU PMEF XXXMPWF E MPHDDPN 57513 2001 Jim Fay End the Bedtime Blues Parents Dont Need to Force Kids to Go to Sleep edtime is a time of frustration Nimantha . Thushan. Baranasuriya. Girisha. . Durrel. De Silva. Rahul . Singhal. Karthik. . Yadati. Ziling. . Zhou. Outline. Random Walks. Markov Chains. Applications. 2SAT. 3SAT. Card Shuffling. (1). Brief . review of discrete time finite Markov . Chain. Hidden Markov . Model. Examples of HMM in Bioinformatics. Estimations. Basic Local Alignment Search Tool (BLAST). The strategy. Important parameters. Sai. Zhang. , . Congle. Zhang. University of Washington. Presented. . by . Todd Schiller. Software bug localization: finding the likely buggy code fragments. A . software. system. (. source code. Alan Ritter. Markov Networks. Undirected. graphical models. Cancer. Cough. Asthma. Smoking. Potential functions defined over cliques. Smoking. Cancer. . Ф. (S,C). False. False. 4.5. False. True. Van Gael, et al. ICML 2008. Presented by Daniel Johnson. Introduction. Infinite Hidden Markov Model (. iHMM. ) is . n. onparametric approach to the HMM. New inference algorithm for . iHMM. Comparison with Gibbs sampling algorithm. notes for. CSCI-GA.2590. Prof. Grishman. Markov Model . In principle each decision could depend on all the decisions which came before (the tags on all preceding words in the sentence). But we’ll make life simple by assuming that the decision depends on only the immediately preceding decision. 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. . and Bayesian Networks. Aron. . Wolinetz. Bayesian or Belief Network. A probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG).. 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. Parag. . Singla. & Raymond J. Mooney. Dept. of Computer Science. University of Texas, Austin. Motivation . [ Blaylock & Allen 2005] . Road Blocked!. Road Blocked!. Heavy Snow; Hazardous Driving. Markov processes in continuous time were discovered long before Andrey Markov's work in the early 20th . centuryin. the form of the Poisson process.. Markov was interested in studying an extension of independent random sequences, motivated by a disagreement with Pavel Nekrasov who claimed independence was necessary for the weak law of large numbers to hold..