Skip to search formSkip to main content>Semantic Scholar Semantic Scholar's Logo

Search

You are currently offline. Some features of the site may not work correctly.

Semantic Scholar uses AI to extract papers important to this topic.

Review

2011

Review

2011

Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence… Expand

Highly Cited

2008

Highly Cited

2008

Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such… Expand

Review

2008

Review

2008

the current edition is intended to provide practitioners with a comprehensive resource for the use of software package Stata… Expand

Review

1998

Review

1998

The Markov chain Monte Carlo (MCMC) method, as a computer-intensive statistical tool, has enjoyed an enormous upsurge in interest… Expand

Highly Cited

1997

Highly Cited

1997

INTRODUCING MARKOV CHAIN MONTE CARLO Introduction The Problem Markov Chain Monte Carlo Implementation Discussion HEPATITIS B: A… Expand

Highly Cited

1997

Highly Cited

1997

Introduction Stochastic simulation Introduction Generation of Discrete Random Quantities Generation of Continuous Random… Expand

Highly Cited

1996

Highly Cited

1996

Highly Cited

1995

Highly Cited

1995

Review

1994

Review

1994

Abstract A critical issue for users of Markov chain Monte Carlo (MCMC) methods in applications is how to determine when it is… Expand

Review

1991

Review

1991

Markov chain Monte Carlo (e. g., the Metropolis algorithm and Gibbs sampler) is a general tool for simulation of complex… Expand