PPT-Joint Inference of Multiple Label Types in Large Networks

Deepayan Chakrabarti deepayfbcom Stanislav Funiak sfuniakfbcom Jonathan Chang jonchangfbcom Sofus A Macskassy sofmacfbcom 1 Profile Inference Profile Hometown Palo Alto. Kathleen Fisher. cs242. Reading: “Concepts in Programming Languages”, Chapter 6. . . Outline. General discussion of types. What is a type?. Compile-time . vs. run-time checking. As . empirical data about sets of related entities accrues, there are more constraints on possible network realizations that can fit the data; in the language of statistical mechanics, the size of the microstate ensemble shrinks, until the underlying network resolves. The network inference method . (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. What is an Articulation (Joint). Point of contact between two bones. **There are three types of joints. 1. Fibrous Joints. Also called “sutures”. These joints are bound tightly together by connective tissue and allows . A general scenario:. Query . variables:. . X. Evidence . (. observed. ) . variables and their values: . E. = . e. . Unobserved . variables: . Y. . Inference problem. : answer questions about the query variables given the evidence variables. Slide . 1. Reasoning Under Uncertainty: Belief Networks. Computer Science cpsc322, Lecture 27. (Textbook . Chpt. 6.3). March, . 22, 2010. CPSC 322, Lecture 2. Slide . 2. Big Picture: R&R systems. Computer and Communication Networks. 2. nd. Edition. Prentice Hall. ISBN: . 0133814742. Copyright © 2015, Pearson Education Inc., . All Rights Reserved. Chapter 14. Tunneling, VPNs, and MPLS Networks. A general scenario:. Query . variables:. . X. Evidence . (. observed. ) . variables and their values: . E. = . e. . Unobserved . variables: . Y. . Inference problem. : answer questions about the query variables given the evidence variables. Chumbley. Laboratory for Social and Neural Systems Research. Institute for Empirical Research in Economics. University of Zurich. . With many thanks for slides & images to:. FIL Methods group. Overview of SPM. Local Primal-Dual Gaps. Dhruv Batra (TTIC). Joint work with: . Daniel . Tarlow. (U Toronto), Sebastian . Nowozin. (MSRC), . Pushmeet. . Kohli. (MSRC), Vladimir . Kolmogorov. (UCL). Overview. Discrete . Theory Overview. John F. Padgett. c. onference on book at. Radcliffe Institute for Advanced Studies. June 30, 2015. Goals of book. To rethink from social science perspective:. Novelty. . especially organizational novelty. Chapter . 2 . Introduction to probability. Please send errata to s.prince@cs.ucl.ac.uk. Random variables. A random variable . x. denotes a quantity that is uncertain. May be result of experiment (flipping a coin) or a real world measurements (measuring temperature). Robert J. . Tempelman. Department of Animal Science. Michigan State University. 1. Outline of talk:. Introduction. Review . of Likelihood Inference . An Introduction to Bayesian Inference. Empirical Bayes Inference. Reading: “Concepts in Programming Languages”, Chapter 6. . . Outline. General discussion of types. What is a type?. Compile-time . vs. run-time checking. Conservative program analysis.