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Markov chain sampling methods for Dirichlet process mixture models
 JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
, 2000
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CircularlyCoupled Markov Chain Sampling
, 2002
"... I show how to run an Ntimestep Markov chain simulation in a circular fashion, so that the state at time 0 follows the state at time N1 in the same way as states at times t follow those at times t1 for 0< t <N . This wraparound of the chain is achieved using a coupling procedure, and produ ..."
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Cited by 8 (2 self)
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I show how to run an Ntimestep Markov chain simulation in a circular fashion, so that the state at time 0 follows the state at time N1 in the same way as states at times t follow those at times t1 for 0< t <N . This wraparound of the chain is achieved using a coupling procedure
A markovchain sampling algorithm for GARCH models
 Studies in Nonlinear Dynamics and Econometrics
, 1998
"... $40.00, Institutions $130.00. Canadians add 7 % GST. Prices subject to change without notice. Subscribers are licensed to use journal articles in a variety of ways, limited only as required to insure fair attribution to authors and the Journal, and to prohibit use in a competing commercial product. ..."
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Cited by 8 (0 self)
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$40.00, Institutions $130.00. Canadians add 7 % GST. Prices subject to change without notice. Subscribers are licensed to use journal articles in a variety of ways, limited only as required to insure fair attribution to authors and the Journal, and to prohibit use in a competing commercial product. See the Journalâ€™s World Wide Web site for further details. Address inquiries to the Subsidiary Rights Manager, MIT
Bayesian analysis of ARMAGARCH models: a Markov chain sampling approach
 Journal of Econometrics
, 2000
"... We develop a Markov chain Monte Carlo method for a linear regression model with an ARMA(p, q)GARCH(r, s) error. To generate a Monte Carlo sample from the joint posterior distribution, we employ a Markov chain sampling with the Metropolis}Hastings algorithm. As illustration, we estimate an ARMA}GARC ..."
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Cited by 27 (2 self)
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We develop a Markov chain Monte Carlo method for a linear regression model with an ARMA(p, q)GARCH(r, s) error. To generate a Monte Carlo sample from the joint posterior distribution, we employ a Markov chain sampling with the Metropolis}Hastings algorithm. As illustration, we estimate an ARMA
Implementing Pure Adaptive Search for Global Optimization using Markov Chain Sampling
 Journal of Global Optimization
, 2001
"... The Pure Adaptive Search (PAS) algorithm for global optimization yields a sequence of points, each of which is uniformly distributed in the level set corresponding to its predecessor.This algorithm has the highly desirable property of solving a large class of global optimization problems using a num ..."
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Cited by 4 (1 self)
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equivalence between generating an approximately uniformly distributed point using Markov chain sampling, and generating an exactly uniformly distributed point with a certain probability.As an application, we use this equivalence to show that PAS, using the socalled Random ball walk Markov chain sampling
Haplotype Inference Using a Hidden Markov Model with Ecient Markov Chain Sampling
"... Abstract: Knowledge of haplotypes is useful for understanding block structure and disease risk associations. Direct measurement of haplotypes in the absence of family data is presently impractical. Hence several methods have been developed previously for reconstructing haplotypes from population dat ..."
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by Bayesian methods using MCMC. Crucial to the eciency of the Markov Chain sampling is the use of a forwardbackward algorithm for summing over all possible state sequences of the HMM. We have used the model to reconstruct the haplotypes of 129 children in the data set of Daly et al [1] and of 30 children
Haplotype inference using a hidden Markov model with efficient Markov chain sampling
, 2007
"... Knowledge of haplotypes is useful for understanding block structures of the genome and finding genes associated with disease. Direct measurement of haplotypes in the absence of family data is presently impractical. Hence several methods have been developed previously for reconstructing haplotypes f ..."
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Cited by 1 (0 self)
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for the genotyping error rate, the mutation rate, and the recombination rate. Four mutation models with varying number of parameters are developed and compared. Parameters of the model are inferred by Bayesian methods, using Markov Chain Monte Carlo (MCMC). Crucial to the efficiency of the Markov chain sampling
Probabilistic Inference Using Markov Chain Monte Carlo Methods
, 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over highdimensional spaces. R ..."
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Cited by 738 (24 self)
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. Related problems in other fields have been tackled using Monte Carlo methods based on sampling using Markov chains, providing a rich array of techniques that can be applied to problems in artificial intelligence. The "Metropolis algorithm" has been used to solve difficult problems in statistical
Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics
, 1996
"... For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain has ..."
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Cited by 548 (13 self)
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For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain
Markov Chain Sampling for Nonlinear State Space Models Using Embedded Hidden Markov Models
, 2003
"... I describe a new Markov chain method for sampling from the distribution of the state sequences in a nonlinear state space model, given the observation sequence. This method updates all states in the sequence simultaneously using an embedded Hidden Markov model (HMM). An update begins with the creat ..."
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Cited by 7 (5 self)
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I describe a new Markov chain method for sampling from the distribution of the state sequences in a nonlinear state space model, given the observation sequence. This method updates all states in the sequence simultaneously using an embedded Hidden Markov model (HMM). An update begins
Results 1  10
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932,085