PPT-S14: Interpretable Probabilistic Latent Variable Models for

Alexander Kotov 1 Mehedi Hasan 1 April Carcone 1 Ming Dong 1 Sylvie NaarKing 1 Kathryn Brogan Hartlieb 2 1 Wayne State University 2 Florida International University. com ABSTRACT Latent variable techniques are pivotal in tasks ranging from predicting user click patterns and targeting ads to organiz ing the news and managing user generated content La tent variable techniques like topic modeling clustering and subs The General Case. STA431: Spring 2013. See last slide for copyright information. An Extension of Multiple Regression. More than one regression-like equation. Includes latent variables. Variables can be explanatory in one equation and response in another. Presented by Zhou Yu. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. A. A. M.Pawan. Kumar Ben Packer Daphne . Koller. , Stanford University. 1. Aim: . Naman Agarwal. Michael Nute. May 1, 2013. Latent Variables. Contents. Definition & Example of Latent Variables. EM Algorithm Refresher. Structured SVM with Latent Variables. Learning under semi-supervision or indirect supervision. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. 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. Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. Indranil Gupta. Associate Professor. Dept. of Computer Science, University of Illinois at Urbana-Champaign. Joint work with . Muntasir. . Raihan. . Rahman. , Lewis Tseng, Son Nguyen, . Nitin. . Vaidya. Trang Quynh Nguyen, May 9, 2016. 410.686.01 Advanced Quantitative Methods in the Social and Behavioral Sciences: A Practical Introduction. Objectives. Provide a QUICK introduction to latent class models and finite mixture modeling, with examples. Alan Nicewander. Pacific Metrics. Presented at a conference to honor . Dr. Michael W. Browne of the Ohio State University, September 9-10, 2010 . Using the factor analytic version of item response (IRT) models, . Peter Congdon, Queen Mary University of London, School of Geography & Life Sciences Institute. Outline. Background. Bayesian approaches: advantages/cautions. Bayesian Computing, Illustrative . BUGS model, Normal Linear . Re-Designing . CS1 for Breadth and Retention. Natalie . Linnell. , Nicholas . Tran, Carol . Gittens. : . Santa . Clara University. Goals of re-design. Make course function better as an elective. Improve retention, esp. women and underrepresented minorities. Nisheeth. Coin toss example. Say you toss a coin N times. You want to figure out its bias. Bayesian approach. Find the generative model. Each toss ~ Bern(. θ. ). θ. ~ Beta(. α. ,. β. ). Draw the generative model in plate notation. Nathan Clement. Computational Sciences Laboratory. Brigham Young University. Provo, Utah, USA. Next-Generation Sequencing. Problem Statement . Map next-generation sequence reads with variable nucleotide confidence to .