'Maximum likelihood estimates' presentation slideshows

Christopher M. Bishop

Latent Variables, Mixture Models and EM. Christopher M. Bishop. Microsoft Research, Cambridge. BCS Summer School Exeter, 2003. Overview. K-means clustering Gaussian mixtures Maximum likelihood and EM Probabilistic graphical models Latent variables: EM revisited

1.73k views • 101 slides

Maximum Likelihood Estimates and the EM Algorithms II

937 views • 69 slides

Practical Statistics for Particle Physicists Lecture 2

586 views • 49 slides

An Introduction to Logistic Regression

An Introduction to Logistic Regression. JohnWhitehead Department of Economics Appalachian State University. Outline. Introduction and Description Some Potential Problems and Solutions Writing Up the Results. Introduction and Description. Why use logistic regression?

862 views • 48 slides

Topics in Microeconometrics Professor William Greene Stern School of Business, New York University at Curtin Business Sc

Topics in Microeconometrics Professor William Greene Stern School of Business, New York University at Curtin Business School Curtin University Perth July 22-24, 2013. 2. Stochastic Frontier Model. Stochastic Frontier Models. Motivation: Factors not under control of the firm

644 views • 47 slides

Maximum Likelihood (ML), Expectation Maximization (EM) Pieter Abbeel UC Berkeley EECS

Maximum Likelihood (ML), Expectation Maximization (EM) Pieter Abbeel UC Berkeley EECS Many slides adapted from Thrun , Burgard and Fox, Probabilistic Robotics. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A A A A. Outline.

653 views • 46 slides

Hidden Markov Model

679 views • 45 slides

Discrete Choice Modeling

William Greene Stern School of Business New York University. Discrete Choice Modeling. 0 Introduction 1 Methodology 2 Binary Choice 3 Panel Data 4 Bivariate Probit 5 Ordered Choice 6 Count Data 7 Multinomial Choice 8 Nested Logit 9 Heterogeneity 10 Latent Class 11 Mixed Logit

604 views • 39 slides

Navigating the parameter space of Bayesian Knowledge Tracing models

Navigating the parameter space of Bayesian Knowledge Tracing models. Visualizations of the convergence of the Expectation Maximization algorithm. Zachary A. Pardos, Neil T. Heffernan. Worcester Polytechnic Institute Department of Computer Science. Outline. Introduction Knowledge Tracing/EM

809 views • 37 slides

Theoretical and experimental comparisons of gene expression indexes for oligonucleotide microarrays

Theoretical and experimental comparisons of gene expression indexes for oligonucleotide microarrays. William J. Lemon, Jeffrey J.T. Palatini, Ralf Krahe, Fred A. Wright. Department of Biostatistics, University pf North Carolina, Chapel Hill

435 views • 33 slides

Markov Chains: Transitional Modeling

Markov Chains: Transitional Modeling. Qi Liu. content. Terminology Transitional Models without Explanatory Variables Inference for Markov chains Data Analysis : Example 1 (ignoring explanatory variables) Transitional Models with Explanatory Variables

491 views • 28 slides

Discrete Choice Modeling

William Greene Stern School of Business IFS at UCL February 11-13, 2004. Discrete Choice Modeling. http://cemmap.ifs.org.uk/resources/files/resources_greene_discrete.shtml. Part 3. Modeling Binary Choice. A Model for Binary Choice. Yes or No decision (Buy/Not buy)

426 views • 27 slides

Empirical/Asymptotic P-values for Monte Carlo-Based Hypothesis Testing: an Application to Cluster Detection Using the Sc

Empirical/Asymptotic P-values for Monte Carlo-Based Hypothesis Testing: an Application to Cluster Detection Using the Scan Statistic. Allyson Abrams, Martin Kulldorff, Ken Kleinman Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care

605 views • 24 slides

The EM algorithm (Part 1)

493 views • 22 slides

Modular and hierarchical learning systems

Modular and hierarchical learning systems. Michael I. Jordan and Robert A. Jacobs Presented by Danke Xie Cognitive Science, UCSD CSE 291s Lawrence Saul 4/26/2007. Outline. Decision Tree Mixture of Experts Architecture The Mixture of Experts Model Learning algorithm

289 views • 20 slides

Northwestern University Department of Industrial Engineering and Management Sciences Assembled Designs for Dispersion Ef

310 views • 19 slides

Statistical NLP: Lecture 10

Statistical NLP: Lecture 10. Lexical Acquisition. Goal of Lexical Acquisition. Goal: To develop algorithms and statistical techniques for filling the holes in existing machine-readable dictionaries by looking at the occurrence patterns of words in large text corpora.

371 views • 18 slides

Labs 6 & 7 Case-Control Analysis ----Logistic Regression Henian Chen, M.D., Ph.D.

Labs 6 & 7 Case-Control Analysis ----Logistic Regression Henian Chen, M.D., Ph.D. Logistic Regression for Intercept only SAS Program proc logistic data =case_control978 descending ; model status=; run ; * Descending: to get the probability and OR for dependent variable=1 SAS Output

305 views • 12 slides

How many transcripts does it take to reconstruct the splice graph?

Introduction

153 views • 1 slides

Default and Recovery Implicit in the Term Structure of Sovereign CDS Spreads -- Jun Pan and Kenneth J. Singleton ( Oc

Default and Recovery Implicit in the Term Structure of Sovereign CDS Spreads -- Jun Pan and Kenneth J. Singleton ( Oct., 2008). Xue (Cindy) Hu 9/26/2013. Agenda. Introduction The Structure of Sovereign CDS Market Pricing Sovereign CDS Contracts Separately Identify L Q and ? Q

74 views • 0 slides