<--- Back to Details
First PageDocument Content
Graphical models / Machine learning / Statistical models / Statistical theory / Expectation propagation / Belief propagation / Bayesian inference / Bayesian network / Variational Bayesian methods / Statistics / Bayesian statistics / Probability and statistics
Date: 2009-01-14 12:50:42
Graphical models
Machine learning
Statistical models
Statistical theory
Expectation propagation
Belief propagation
Bayesian inference
Bayesian network
Variational Bayesian methods
Statistics
Bayesian statistics
Probability and statistics

Add to Reading List

Source URL: research.microsoft.com

Download Document from Source Website

File Size: 398,54 KB

Share Document on Facebook

Similar Documents

Probability and statistics / Statistical theory / Statistics / Graphical models / Bayesian statistics / Market research / Market segmentation / Machine learning / Factor graph / Belief propagation / Variational Bayesian methods / Mixture model

Learning to Pass Expectation Propagation Messages Nicolas Heess∗ Gatsby Unit, UCL Daniel Tarlow Microsoft Research

DocID: 1xTL0 - View Document

Just-In-Time Kernel Regression for Expectation Propagation Wittawat Jitkrittum1 Arthur Gretton1 Nicolas Heess∗ S. M. Ali Eslami∗

DocID: 1vbc5 - View Document

Bayesian statistics / Statistics / Graphical models / Probability / Markov models / Bayesian network / Probability distribution / Markov chain / Expectation propagation / Approximate inference / Likelihood function / Hidden Markov model

Mean Field Variational Approximations in Continuous-Time Markov Processes A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science

DocID: 1rlyc - View Document

Graphical models / Probability distributions / Bayesian network / Networks / Normal distribution / Variational Bayesian methods / Belief propagation / Exponential family

On the concentration of expectation and approximate inference in layered networks Michael I. Jordan University of California Berkeley, CA 94720

DocID: 1o0ol - View Document

Derivation of Expectation Propagation for “Fast Gaussian Process Methods for Point Process Intensity Estimation” John P. Cunningham Department of Electrical Engineering, Stanford University, Stanford, CA 94305

DocID: 1nXEp - View Document