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Markov chain Monte Carlo for exact inference for diffusions
Exact inference Exact simulation Markov chain Monte Carlo Stochastic differential equa-tion Transition density
2011/3/25
We develop exact Markov chain Monte Carlo methods for discretely-sampled, directly and indirectly observed diffusions. The qualification "exact" refers to the fact that the invariant and limiting dist...
Weak Convergence of Markov Chain Monte Carlo Methods and its Application to Regular Gibbs Sampler
Ergodicity Gibbs sampler Large sample theory Markov chain
2011/3/2
In this paper, we introduce the notion of efficiency (consistency) and examine some asymptotic properties ofMarkov chain Monte Carlo methods. We apply these results to the Gibbs sampler for independen...
A general purpose variance reduction technique for Markov chain Monte Carlo estimators based on the zero-variance principle introduced in the physics literature by Assaraf and Caffarel (1999, 2003), i...
Weak Convergence of Markov Chain Monte Carlo Methods and its Application to Regular Gibbs Sampler
Methodology (stat.ME) Statistics Theory (math.ST)
2010/12/17
In this paper, we introduce the notion of efficiency (consistency) and examine some asymptotic properties of Markov chain Monte Carlo methods. We apply these results to the Gibbs sampler for independe...
Interacting Markov chain Monte Carlo methods for solving nonlinear measure-valued equations
Markov chain Monte Carlo methods sequential Monte Carlo methods
2010/12/14
We present a new class of interacting Markov chain Monte Carlo algorithms for solving numerically discrete-time measure-valued equa-tions. The associated stochastic processes belong to the class of se...
Model Selection and Adaptive Markov chain Monte Carlo for Bayesian Cointegrated VAR model
Markov chain Monte Carlo Bayesian Cointegrated VAR model
2010/10/19
This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesia...
Model Selection and Adaptive Markov chain Monte Carlo for Bayesian Cointegrated VAR model
Cointegrated Vector Auto Regressions Markov chain Monte Carlo
2010/4/28
This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesian...
The reversible jump Markov chain Monte Carlo sampler (Green, 1995) provides a general
framework for Markov chain Monte Carlo (MCMC) simulation in which the dimension of the
parameter space can vary ...
In Bayesian inference, the posterior distribution for parameters 2 is given by (jy) /
(yj)(), where one's prior beliefs about the unknown parameters, as expressed through
the prior distrib...
A History of Markov Chain Monte Carlo——Subjective Recollections from Incomplete Data
History Markov Chain Monte Carlo——Subjective Recollections Incomplete Data
2010/4/30
In this note we attempt to trace the history and development of Markov chain
Monte Carlo (MCMC) from its early inception in the late 1940’s through its use today.
We see how the earlier stages of th...
Parameter Estimation in Continuous Time Markov Switching Models: A Semi-Continuous Markov Chain Monte Carlo Approach
Bayesian inference data augmentation hidden Markov model
2009/9/24
In this paper,we combine useful aspects of both approaches.On the one hand,we are inspired by the discretization, where filtering for the state process is possible,on the other hand,we
catch attracti...
EM versus Markov chain Monte Carlo for estimation of hidden Markov models: a computational perspective
hidden Markov model incomplete data missing data EM trans-dimensional Monte Carlo computational statistics
2009/9/22
Hidden Markov models (HMMs) and related models have become stan-
dard in statistics during the last 15C2 years, with applications in diverse areas
like speech and other statistical signal processing...
How to Combine Fast Heuristic Markov Chain Monte Carlo with Slow Exact Sampling
Confidence interval Exact sampling Markov Chain Monte Carlo
2009/5/4
Given a probability law $pi$ on a set S and a function $g : S rightarrow R$, suppose one wants to estimate the mean $bar{g} = int g dpi$. The Markov Chain Monte Carlo method consists of inventing and ...
Implementation of a Markov Chain Monte Carlo method to inorganic aerosol modeling of observations from the MCMA-2003 campaign – Part II: Model application to the CENICA, Pedregal and Santa Ana sites
Markov Chain Monte Carlo method inorganic aerosol CENICA Pedregal Santa Ana sites
2009/1/16
A Markov Chain Monte Carlo model for integrating the observations of inorganic species with a thermodynamic equilibrium model was presented in Part I of this series. Using observations taken at three ...
Implementation of a Markov Chain Monte Carlo method to inorganic aerosol modeling of observations from the MCMA-2003 campaign – Part I: Model description and application to the La Merced site
Markov Chain Monte Carlo method inorganic aerosol modeling Model description La Merced site
2009/1/16
The equilibrium inorganic aerosol model ISORROPIA was embedded in a Markov Chain Monte Carlo algorithm to develop a powerful tool to analyze aerosol data and predict gas phase concentrations where the...