• Publications
  • Influence
Statistical and Computational Inverse Problems
Classification Without Interaction”), and 13 (“Two-Way Crossed Classification With Interaction”). Every chapter contains two or more numerical example with the exception of Chapters 14 (“Three-WayExpand
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The Multiple-Try Method and Local Optimization in Metropolis Sampling
Abstract This article describes a new Metropolis-like transition rule, the multiple-try Metropolis, for Markov chain Monte Carlo (MCMC) simulations. By using this transition rule together withExpand
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Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models
We propose an evolutionary Monte Carlo algorithm to sample from a target distribution with real-valued parameters. The attractive features of the algorithm include the ability to learn from theExpand
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Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples
Preface. Acknowledgments. Publisher's Acknowledgments. 1 Bayesian Inference and Markov Chain Monte Carlo. 1.1 Bayes. 1.1.1 Specification of Bayesian Models. 1.1.2 The Jeffreys Priors and Beyond. 1.2Expand
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Stochastic Approximation in Monte Carlo Computation
The Wang–Landau (WL) algorithm is an adaptive Markov chain Monte Carlo algorithm used to calculate the spectral density for a physical system. A remarkable feature of the WL algorithm is that it isExpand
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Evolutionary Monte Carlo for protein folding simulations
We demonstrate that evolutionary Monte Carlo (EMC) can be applied successfully to simulations of protein folding on simple lattice models, and to finding the ground state of a protein. In all cases,Expand
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A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants
The problem of simulating from distributions with intractable normalizing constants has received much attention in recent literature. In this article, we propose an asymptotic algorithm, theExpand
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EVOLUTIONARY MONTE CARLO: APPLICATIONS TO Cp MODEL SAMPLING AND CHANGE POINT PROBLEM
TLDR
We propose a new Markov chain Monte Carlo algorithm called an evolutionary Monte Carlo. Expand
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Nearly optimal Bayesian Shrinkage for High Dimensional Regression
During the past decade, shrinkage priors have received much attention in Bayesian analysis of high-dimensional data. In this paper, we study the problem for high-dimensional linear regression models.Expand
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Bayesian neural networks for nonlinear time series forecasting
  • F. Liang
  • Computer Science
  • Stat. Comput.
  • 2005
TLDR
We apply Bayesian neural networks (BNNs) to time series analysis, and propose a Monte Carlo algorithm for BNN training. Expand
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