<--- Back to Details
First PageDocument Content
Statistical inference / Philosophy of science / Statistical models / Bayesian inference / Dirichlet process / Frequentist inference / Exchangeable random variables / Parametric model / Hierarchical Bayes model / Statistics / Bayesian statistics / Statistical theory
Date: 2009-06-18 10:03:04
Statistical inference
Philosophy of science
Statistical models
Bayesian inference
Dirichlet process
Frequentist inference
Exchangeable random variables
Parametric model
Hierarchical Bayes model
Statistics
Bayesian statistics
Statistical theory

Applied Nonparametric Bayes Michael I. Jordan Department of Electrical Engineering and Computer Science Department of Statistics University of California, Berkeley http://www.cs.berkeley.edu/∼jordan

Add to Reading List

Source URL: www.siam.org

Download Document from Source Website

File Size: 639,12 KB

Share Document on Facebook

Similar Documents

Hierarchical Double Dirichlet Process Mixture of Gaussian Processes Aditya Tayal and Pascal Poupart and Yuying Li {amtayal, ppoupart, yuying}@uwaterloo.ca Cheriton School of Computer Science University of Waterloo Waterl

Hierarchical Double Dirichlet Process Mixture of Gaussian Processes Aditya Tayal and Pascal Poupart and Yuying Li {amtayal, ppoupart, yuying}@uwaterloo.ca Cheriton School of Computer Science University of Waterloo Waterl

DocID: 1uDDP - View Document

MPME-DP: Multi-Population Moment Estimation via Dirichlet Process for Efficient Validation of Analog/Mixed-Signal Circuits

MPME-DP: Multi-Population Moment Estimation via Dirichlet Process for Efficient Validation of Analog/Mixed-Signal Circuits

DocID: 1uBIS - View Document

Learning stick-figure models using nonparametric Bayesian priors over trees Edward W. Meeds, David A. Ross, Richard S. Zemel, and Sam T. Roweis Department of Computer Science University of Toronto {ewm, dross, zemel, row

Learning stick-figure models using nonparametric Bayesian priors over trees Edward W. Meeds, David A. Ross, Richard S. Zemel, and Sam T. Roweis Department of Computer Science University of Toronto {ewm, dross, zemel, row

DocID: 1roJh - View Document

A Latent Dirichlet Model for Unsupervised Entity Resolution Indrajit Bhattacharya Lise Getoor Department of Computer Science University of Maryland, College Park, MDAbstract

A Latent Dirichlet Model for Unsupervised Entity Resolution Indrajit Bhattacharya Lise Getoor Department of Computer Science University of Maryland, College Park, MDAbstract

DocID: 1qMC3 - View Document

A Hierarchical Dirichlet Process Model with Multiple Levels of Clustering for Human EEG Seizure Modeling Drausin Wulsin Dept. of Bioengineering, University of Pennsylvania, Philadelphia, PA USA Shane Jensen

A Hierarchical Dirichlet Process Model with Multiple Levels of Clustering for Human EEG Seizure Modeling Drausin Wulsin Dept. of Bioengineering, University of Pennsylvania, Philadelphia, PA USA Shane Jensen

DocID: 1qwtO - View Document