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» Feature Correspondence: A Markov Chain Monte Carlo Approach
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NIPS
2007
13 years 9 months ago
Markov Chain Monte Carlo with People
Many formal models of cognition implicitly use subjective probability distributions to capture the assumptions of human learners. Most applications of these models determine these...
Adam Sanborn, Thomas L. Griffiths
DATE
2010
IEEE
171views Hardware» more  DATE 2010»
14 years 17 days ago
Statistical static timing analysis using Markov chain Monte Carlo
—We present a new technique for statistical static timing analysis (SSTA) based on Markov chain Monte Carlo (MCMC), that allows fast and accurate estimation of the right-hand tai...
Yashodhan Kanoria, Subhasish Mitra, Andrea Montana...
UAI
2001
13 years 8 months ago
Markov Chain Monte Carlo using Tree-Based Priors on Model Structure
We present a general framework for defining priors on model structure and sampling from the posterior using the Metropolis-Hastings algorithm. The key ideas are that structure pri...
Nicos Angelopoulos, James Cussens
AUTOMATICA
2010
122views more  AUTOMATICA 2010»
13 years 7 months ago
Bayesian system identification via Markov chain Monte Carlo techniques
The work here explores new numerical methods for supporting a Bayesian approach to parameter estimation of dynamic systems. This is primarily motivated by the goal of providing ac...
Brett Ninness, Soren J. Henriksen
ICML
2008
IEEE
14 years 8 months ago
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP...
Ruslan Salakhutdinov, Andriy Mnih