PDF-Fast Exact Inference for Recursive Cardinality Models

Zemel Dept of Computer Science University of Toronto dtarlowkswerskyzemel cstorontoedu Ryan P Adams Sch of Eng Appl Sci Harvard University rpaseasharvardedu Brendan J Frey Prob Stat Inf Group University of Toronto freypsitorontoedu Abstract Cardin. 1093panmpr013 Causal Inference without Balance Checking Coarsened Exact Matching Stefano M Iacus Department of Economics Business and Statistics University of Milan Via Conservatorio 7 I20124 Mila Inference. Basic task for inference:. Compute a posterior distribution for some query variables given some observed evidence. Sum out nuisance variables. In general inference in GMs is intractable…. Graphical Model Inference. View observed data and unobserved properties as . random variables. Graphical Models: compact graph-based encoding of probability distributions (high dimensional, with complex dependencies). Chapter 14 . The pinhole camera. Structure. Pinhole camera model. Three geometric problems. Homogeneous coordinates. Solving the problems. Exterior orientation problem. Camera calibration. 3D reconstruction. Source: “Topic models”, David . Blei. , MLSS ‘09. Topic modeling - Motivation. Discover topics from a corpus . Model connections between topics . Model the evolution of topics over time . Image annotation. Chapter 5 . The Normal Distribution. Univariate. Normal Distribution. For short we write:. Univariate. normal distribution describes single continuous variable.. Takes 2 parameters . m. and . s. 2. Leo Zhu. CSAIL MIT . Joint work with Chen, Yuille, Freeman and Torralba . 1. Ideas behind . Recursive Composition . How to deal with image complexity. A general framework for different vision tasks. Rich representation and tractable computation. Definition. : The . cardinality. of a set . A. is equal to the cardinality of a set . B. , denoted . . |A| = |. B. |,. if and only if there is a one-to-one correspondence (. Thesis defense . 4/5/2012. Jaesik Choi. Thesis Committee: . Assoc. Prof. Eyal Amir (Chair, Director of research). Prof. Dan Roth. . Prof. Steven M. Lavalle. Prof. David Poole (University of British Columbia). With thanks to: . Parisa . Kordjamshidi, Avi Pfeffer, Guy Van den . Broeck. , Sameer Singh,  . Vivek Srikumar, Rodrigo de Salvo Braz,. . Nick Rizzolo .   . Declarative . Learning Based Programming. 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). Chapter 19 . Temporal models. 2. Goal. To track object state from frame to frame in a video. Difficulties:. Clutter (data association). One image may not be enough to fully define state. Relationship between frames may be complicated. Sriraam Natarajan. Dept of . Computer Science, . University . of . Wisconsin-Madison. Take-Away Message . Inference. in SRL Models is . very hard. !!!!. This talk – Presents . 3 different yet related.  . With Bayesian conditional density estimation. Problem. Analytic expressions for likelihood of parameters is not available with simulation based models. Approximate Bayesian Computation (ABC). Provides likelihood free inference.