Results 1 - 10 of 2,410

Graphical models, exponential families, and variational inference

by Martin J. Wainwright, Michael I. Jordan , 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
Abstract - Cited by 819 (28 self) - Add to MetaCart
all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.

Time varying structural vector autoregressions and monetary policy

by Giorgio E. Primiceri - REVIEW OF ECONOMIC STUDIES , 2005
"... Monetary policy and the private sector behavior of the US economy are modeled as a time varying structural vector autoregression, where the sources of time variation are both the co-efficients and the variance covariance matrix of the innovations. The paper develops a new, simple modeling strategy f ..."
Abstract - Cited by 306 (8 self) - Add to MetaCart
for the law of motion of the variance covariance matrix and proposes an efficient Markov chain Monte Carlo algorithm for the model likelihood/posterior numerical evaluation. The main empirical conclusions are: 1) both systematic and non-systematic mone-tary policy have changed during the last forty years

Variational inference for Dirichlet process mixtures

by David M. Blei, Michael I. Jordan - Bayesian Analysis , 2005
"... Abstract. Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled the application of nonparametric Bayesian methods to a variety of practical data analysis prob ..."
Abstract - Cited by 244 (27 self) - Add to MetaCart
Abstract. Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled the application of nonparametric Bayesian methods to a variety of practical data analysis

Quasi-Monte Carlo Integration

by William J. Morokoff, Russel E. Caflisch - JOURNAL OF COMPUTATIONAL PHYSICS , 1995
"... The standard Monte Carlo approach to evaluating multi-dimensional integrals using (pseudo)-random integration nodes is frequently used when quadrature methods are too difficult or expensive to implement. As an alternative to the random methods, it has been suggested that lower error and improved con ..."
Abstract - Cited by 73 (6 self) - Add to MetaCart
properties of the integrand, including variance, variation, smoothness and dimension. The results show that variation, which plays an important role in the theoretical upper bound given by the Koksma-Hlawka inequality, does not affect convergence; while variance, the determining factor in random Monte Carlo

Driving Markov Chain Monte Carlo with a Dependent Random Stream

by Iain Murray, Lloyd T. Elliott , 2012
"... Summary. Markov chain Monte Carlo is a widely-used technique for generating a dependent sequence of samples from complex distributions. Conventionally, these methods require a source of independent random variates. Most implementations use pseudo-random numbers instead because generating true indepe ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
Summary. Markov chain Monte Carlo is a widely-used technique for generating a dependent sequence of samples from complex distributions. Conventionally, these methods require a source of independent random variates. Most implementations use pseudo-random numbers instead because generating true

A survey of Monte Carlo tree search methods

by Cameron Browne, Edward Powley, Daniel Whitehouse, Simon Lucas, Peter I. Cowling, Stephen Tavener, Diego Perez, Spyridon Samothrakis, Simon Colton, et al. - IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI , 2012
"... Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a ra ..."
Abstract - Cited by 104 (18 self) - Add to MetaCart
Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a

Statistical timing analysis considering spatial correlations using a single PERT-like traversal

by Hongliang Chang, Sachin S. Sapatnekar - In ICCAD
"... We present an efficient statistical timing analysis algorithm that predicts the probability distribution of the circuit delay while incorporating the effects of spatial correlations of intra-die parameter variations, using a method based on principal component analysis. The method uses a PERT-like c ..."
Abstract - Cited by 236 (17 self) - Add to MetaCart
Monte Carlo simulation. delays), as explained in Section 2. Moreover, any strictly pathbased method will eventually be faced with an explosion in the number of critical paths. We propose an algorithm for statistical STA that computes the distribution of circuit delay while considering correlations due

Control variates for quasi-Monte Carlo

by Fred Hickernell , Christiane Lemieux, Art B. Owen , 2003
"... Quasi-Monte Carlo (QMC) methods have begun to displace ordinary Monte Carlo (MC) methods in many practical problems. It is natural and obvious to combine QMC methods with traditional variance reduction techniques used in MC sampling, such as control variates. There can, ..."
Abstract - Cited by 11 (3 self) - Add to MetaCart
Quasi-Monte Carlo (QMC) methods have begun to displace ordinary Monte Carlo (MC) methods in many practical problems. It is natural and obvious to combine QMC methods with traditional variance reduction techniques used in MC sampling, such as control variates. There can,

Delayed rejection variational Monte Carlo

by Dario Bressanini , Gabriele Morosi , Silvia Tarasco , Antonietta Mira , 2004
"... An acceleration algorithm to address the problem of multiple time scales in variational Monte Carlo simulations is presented. After a first attempted move has been rejected, the delayed rejection algorithm attempts a second move with a smaller time step, so that even moves of the core electrons can ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
An acceleration algorithm to address the problem of multiple time scales in variational Monte Carlo simulations is presented. After a first attempted move has been rejected, the delayed rejection algorithm attempts a second move with a smaller time step, so that even moves of the core electrons

K-ANTITHETIC VARIATES IN MONTE CARLO

by Abdelaziz Nasroallah
"... Abstract. Standard Monte Carlo simulation needs prohibitive time to achieve reasonable estimations. for untractable integrals (i.e. multidimen-sional integrals and/or intergals with complex integrand forms). Several statistical technique, called variance reduction methods, are used to reduce the sim ..."
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Abstract. Standard Monte Carlo simulation needs prohibitive time to achieve reasonable estimations. for untractable integrals (i.e. multidimen-sional integrals and/or intergals with complex integrand forms). Several statistical technique, called variance reduction methods, are used to reduce
Results 1 - 10 of 2,410