PPT-Fast -free Inference of Simulation Models

  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. B. . Moetakef-Imani. , . N.Z.Yussefian. . 10 July 2009. Aaron Boyd. October 28, 2009. FUNCTION. To simulated the dynamic response in boring processes. To predict vibrations in the operation. Why Important?. 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). Rahul Sharma and Alex Aiken (Stanford University). 1. Randomized Search. x. = . i. ;. y = j;. while . y!=0 . do. . x = x-1;. . y = y-1;. if( . i. ==j ). assert x==0. No!. Yes!.  . 2. Invariants. Kari Lock Morgan. Department of Statistical Science, Duke University. kari@stat.duke.edu. . with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock. ECOTS. 5/16/12. Hypothesis Testing:. Use a formula to calculate a test statistic. . and Randomization Procedures. Dennis Lock. Statistics Education Meeting. October 30, 2012. 1. An introductory statistics book writing with my family. Robin H. Lock (St. Lawrence). Patti F. Lock (St. Lawrence). Chris . Mathys. Wellcome Trust Centre for Neuroimaging. UCL. SPM Course. London, May 11, 2015. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. 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. Kari Lock Morgan, Penn State . Robin Lock, St. Lawrence University. Patti Frazer Lock, St. Lawrence University. USCOTS 2015. Overview. We use simulation-based methods to introduce the key ideas of inference. 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. 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. , . Introduction. In the previous . chapter, . we introduced most of the important concepts for developing . and analyzing . spreadsheet simulation models. . We . also discussed many of the features . available in . Simulation can be used to analyze a . wide variety . of problems.. The applications can . be grouped into four general . areas:. Operations models. Financial models. Marketing models. Games of chance. 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. Simulation Models 16 Introduction Simulation can be used to analyze a wide variety of problems. The applications can be grouped into four general areas: Operations models Financial models Marketing models