'Latent variable models' presentation slideshows

Probabilistic Models for Matrix Completion Problems

Probabilistic Models for Matrix Completion Problems. Arindam Banerjee banerjee@cs.umn.edu Dept of Computer Science & Engineering University of Minnesota, Twin Cities. March 11, 2011. Recommendation Systems. Movies. Title: Gone with the wind Release year: 1940

1.03k views • 65 slides

A Cognitive Diagnosis Model for Cognitively-Based Multiple-Choice Options

A Cognitive Diagnosis Model for Cognitively-Based Multiple-Choice Options. Jimmy de la Torre Department of Educational Psychology Rutgers, The State University of New Jersey. All wrong answers are wrong;. But some wrong answers are more wrong than others. Introduction.

828 views • 58 slides

Structural Equation Modeling: An Overview

692 views • 39 slides

Lecture 17 Gaussian Mixture Models and Expectation Maximization

Lecture 17 Gaussian Mixture Models and Expectation Maximization. Machine Learning. Last Time. Logistic Regression. Today. Gaussian Mixture Models Expectation Maximization. The Problem. You have data that you believe is drawn from n populations

582 views • 34 slides

Big Issues

Considerations for the choice and interpretation of endpoints in international trials of antiretroviral therapies Victor De Gruttola. Big Issues. When do results from a study in one region apply to another region?

312 views • 23 slides

Limited Dependent Variables

Limited Dependent Variables. P ( y = 1| x ) = G ( b 0 + x b ) y* = b 0 + x b + u, y = max ( 0,y* ). Binary Dependent Variables. Recall the linear probability model, which can be written as P( y = 1| x ) = b 0 + x b

255 views • 15 slides

Limited Dependent Variables

Limited Dependent Variables. P ( y = 1| x ) = G ( b 0 + x b ) y* = b 0 + x b + u, y = max ( 0,y* ). Binary Dependent Variables. Recall the linear probability model, which can be written as P( y = 1| x ) = b 0 + x b

202 views • 15 slides

Max-Margin Minimum Entropy Models

h. x. y. h. x. Max-Margin Minimum Entropy Models. Kevin Miller, M. Pawan Kumar, Ben Packer, Danny Goodman, and Daphne Koller . Generalized Distribution. Experiments. Aim: To learn an accurate set of parameters for latent variable models.

159 views • 1 slides