Results 41 to 50 of about 67,883,804 (80)
An adaptive sequential Monte Carlo sampler [PDF]
, 2013 Sequential Monte Carlo (SMC) methods are not only a popular tool in the analysis of state–space models, but offer an alternative to Markov chain Monte Carlo (MCMC) in situations where Bayesian inference must proceed via simulation.
Fearnhead, Paul, Taylor, Benjamin M.
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Bayesian mixture modelling and inference based Thompson sampling in Monte-Carlo tree search
, 2013 Monte-Carlo tree search is drawing great interest in the domain of planning under uncertainty, particularly when little or no domain knowledge is available. One of the central problems is the trade-off between exploration and exploitation.
Wu, Feng, Bai, Aijun, Chen, Xiaoping
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Media overlay construction via a Markov chain Monte Carlo method
, 2006 Media overlay construction via a Markov chain Monte Carlo ...
D Tsang (7744517) +2 more
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Particle Filters for Markov Switching Stochastic Volatility Models [PDF]
This paper proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain.
Boda Kang, Yun Bao, Carl Chiarella
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SMCTC: Sequential Monte Carlo in C++ [PDF]
Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature.
Adam M. Johansen
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Bayesian Inference for PCFGs via Markov Chain Monte Carlo [PDF]
, 2007 This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an alternative to maximum-likelihood estimation using Inside-Outside algorithm.
Johnson, Mark +2 more
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Determinants of the use of health care services: multilevel analysis in the Metropolitan Region of Sao Paulo. [PDF]
Rev Saude Publica, 2015 Chiavegatto Filho AD +5 more
europepmc +1 more source
"Leverage, heavy-tails and correlated jumps in stochastic volatility models" [PDF]
This paper proposes the efficient and fast Markov chain Monte Carlo estimation methods for the stochastic volatility model with leverage effects, heavy-tailed errors and jump components, and for the stochastic volatility model with correlated jumps.
Jouchi Nakajima, Yasuhiro Omori
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Parallel hierarchical sampling : a general-purpose class of multiple-chains MCMC algorithms [PDF]
, 2009 This paper introduces the Parallel Hierarchical Sampler (PHS), a class of Markov chain Monte Carlo algorithms using several interacting chains having the same target distribution but different mixing properties. Unlike any single-chain MCMC algorithm,
Mira, Antonietta +3 more
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Bayesian inference for Hidden Markov Model [PDF]
Hidden Markov Models can be considered an extension of mixture models, allowing for dependent observations. In a hierarchical Bayesian framework, we show how Reversible Jump Markov Chain Monte Carlo techniques can be used to estimate the parameters of ...
Luisa Scaccia, Rosella Castellano
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