'Marginal likelihood' presentation slideshows

Learning Bayesian Networks from Data

Learning Bayesian Networks from Data. Nir Friedman Daphne Koller Hebrew U. Stanford . Overview. Introduction Parameter Estimation Model Selection Structure Discovery Incomplete Data Learning from Structured Data. Qualitative part :

1.39k views • 113 slides

Crash Course on Machine Learning Part VI

Crash Course on Machine Learning Part VI. Several slides from Derek Hoiem , Ben Taskar , Christopher Bishop, Lise Getoor. Graphical Models . Representations Bayes Nets Markov Random Fields Factor Graphs Inference Exact Variable Elimination BP for trees Approximate inference

719 views • 59 slides

Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA). Week 8. Motivation. What do we want to do with text corpora? classification, novelty detection, summarization and similarity/relevance judgments. Given a text corpora or other collection of discrete data we wish to: Find a short description of the data.

1k views • 55 slides

Structure Learning in Bayesian Networks

Structure Learning in Bayesian Networks. Eran Segal Weizmann Institute. Structure Learning Motivation. Network structure is often unknown Purposes of structure learning Discover the dependency structure of the domain

578 views • 53 slides

Bayesian Learning

Bayesian Learning. Thanks to Nir Friedman, HU. sensor. Example. Suppose we are required to build a controller that removes bad oranges from a packaging line Decision are made based on a sensor that reports the overall color of the orange. Bad oranges. Classifying oranges.

430 views • 41 slides

Bayesian Learning

Bayesian Learning. Thanks to Nir Friedman, HU. sensor. Example. Suppose we are required to build a controller that removes bad oranges from a packaging line Decision are made based on a sensor that reports the overall color of the orange. Bad oranges. Classifying oranges.

411 views • 41 slides

Memoized Online Variational Inference for Dirichlet Process Mixture Models

Memoized Online Variational Inference for Dirichlet Process Mixture Models. NIPS 2013 Michael C. Hughes and Erik B. Sudderth. Motivation. k=6. k=1 k=2 k=3 Clusters: Points:

654 views • 30 slides

Bayes Factor

Bayes Factor. Based on Han and Carlin (2001, JASA). Model Selection. Data: y : finite set of competing models : a distinct unknown parameter vector of dimension n j corresponding to the j th model Prior : all possible values for

521 views • 29 slides

Bayes Factor

Bayes Factor. Based on Han and Carlin (2001, JASA). Model Selection. Data: y : finite set of competing models : a distinct unknown parameter vector of dimension n j corresponding to the j th model Prior : all possible values for

412 views • 29 slides

Bayes Factor

Bayes Factor. Based on Han and Carlin (2001, JASA). Model Selection. Data: y : finite set of competing models : a distinct unknown parameter vector of dimension n j corresponding to the j th model Prior : all possible values for

292 views • 29 slides

Discussion by: Lawrence Christiano

Marco Del Negro, Frank Schorfheide, Frank Smets, and Raf Wouters (DSSW) On the Fit of New-Keynesian Models. Discussion by: Lawrence Christiano. Objective:. Provide a scalar measure of the fit of a Dynamic, Stochastic, General Equilibrium Model (DSGE).

411 views • 27 slides

Loopy belief propagation and probabilistic image processing

Loopy belief propagation and probabilistic image processing. K. Tanaka (Tohoku University, Japan) J. Inoue (Hokkaido University, Japan) D. M. Titterington (University of Glasgow, UK). Image Processing and Magnetic Material. Regular lattice consisting of a lot of nodes.

545 views • 25 slides

Sparse Approximations to Bayesian Gaussian Processes

Sparse Approximations to Bayesian Gaussian Processes. Matthias Seeger University of Edinburgh. Collaborators. Neil Lawrence (Sheffield) Chris Williams (Edinburgh) Ralf Herbrich (MSR Cambridge). Overview of the Talk. Gaussian processes and approximations

367 views • 24 slides

Clustering Short Gene Expression Profiles

Clustering Short Gene Expression Profiles. Ling Wang Marco Ramoni Paolo Sebastiani. The Problem: Input. Gene expression profiles for J genes from microarray experiments. [1]. The Problem: Output. A clustering of the genes that groups functionally related genes in the same cluster.

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Bayesian Nonparametric Matrix Factorization for Recorded Music

Bayesian Nonparametric Matrix Factorization for Recorded Music. Matthew D. Hoffman, David M. Blei, Perry R. Cook. Presented by Lu Ren Electrical and Computer Engineering Duke University. Outline. Introduction . GaP-NMF Model Variational Inference Evaluation Related Work Conclusions.

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Predictive Automatic Relevance Determination by Expectation Propagation

Predictive Automatic Relevance Determination by Expectation Propagation. Y. Qi T.P. Minka R.W. Picard Z. Ghahramani. Motivation. Task 1: Classify high dimensional datasets with many irrelevant features e.g., normal v.s. cancer microarray data.

284 views • 15 slides

Previously summarized by Yung-Kyun Noh Updated and presented by Rhee, Je-Keun

Ch 3. Linear Models for Regression (2/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006. Previously summarized by Yung-Kyun Noh Updated and presented by Rhee, Je-Keun Biointelligence Laboratory, Seoul National University http://bi.snu.ac.kr/. Contents.

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Summarized by Yung-Kyun Noh Biointelligence Laboratory, Seoul National University

Ch 3. Linear Models for Regression (2/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006. Summarized by Yung-Kyun Noh Biointelligence Laboratory, Seoul National University http://bi.snu.ac.kr/. Contents. 3.4 Bayesian Model Comparison 3.5 The Evidence Approximation

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90 views • 1 slides

3 Experiments 3.1 Data Binary cancer classification

cancer. no. samples. no. genes. task. leukemia. 72. 7192. 2 subtypes. colon. 62. 2000. disease/normal. Variable selection using linear sparse Bayesian models for medical classification problems Chuan LU Dept. of Electrical Engineering. 1 Introduction

94 views • 1 slides