PPT-A quick intro to latent class and finite mixture modeling

Trang Quynh Nguyen May 9 2016 41068601 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. Hongning Wang, . Yue. Lu, . ChengXiang. . Zhai. {. wang296,yuelu2,czhai. }@cs.uiuc.edu. Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL, 61801 USA. 1. Kindle 3. iPad. Department of Economics. Stern School of Business. New York University. Latent Class Modeling. Outline. Finite mixture and latent class models . Extensions of the latent class model. Applications of several variations. Alan Ritter. Latent Variable Models. Previously: learning parameters with fully observed data. Alternate approach: hidden (latent) variables. Latent Cause. Q: how do we learn parameters?. Unsupervised Learning. 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 . C. ontents:. Phases of matter. Changing phase. Latent heat. Graphs of phase change. Whiteboard. Graph whiteboards. 4 Phases of Matter. TOC. Solid. Crystalline/non crystalline. Liquid. Greased marbles. Part II: Definition and Properties. Nevin. L. Zhang. Dept. of Computer Science & Engineering. The Hong Kong Univ. of Sci. & Tech.. http://www.cse.ust.hk/~lzhang. AAAI 2014 Tutorial. Part II: Concept . Machine Learning. April 13, 2010. Last Time. Review of Supervised Learning. Clustering. K-means. Soft K-means. Today. A brief look at Homework 2. Gaussian Mixture Models. Expectation Maximization. The Problem. Latent Variables (. LV) in Comparative Effectiveness (CE) research. . We . emphasize the visual modeling approach of statistical questions about CE of alternative . treatments (or interventions); we . These areas have extra notes to help you.. Make notes as we go along, always including these post-its. Notes. Objectives. Objectives. BRONZE. To define ‘latent heat’. SILVER. To be able to measure latent heat. Bergen, August 2009. Roy Howell. Texas Tech University. Latent . Variables, Constructs, and . Constructions.  . First, some acknowledgements:. Einar Breivik, whose questions made me change my thinking about the idea of formative measurement (after 20 years of being wrong). October 28, 2016. Objectives. For you to leave here knowing…. What is the LCR model and its underlying assumptions?. How are LCR parameters interpreted?. How does one check the assumptions of an LCR model?. 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 . – . 2. Introduction. Many linear inverse problems are solved using a Bayesian approach assuming Gaussian distribution of the model.. We show the analytical solution of the Bayesian linear inverse problem in the Gaussian mixture case..