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The Markov Chain Monte Carlo Revolution
This paper offers a personal review of some things we’ve learned about rates of convergence of Markov chains to their stationary distributions. The main topic is ways of speeding up diffusive behavior...
de Finetti Priors using Markov chain Monte Carlo computations。
Computation of the marginal likelihood from a simulated posterior distribution is central to Bayesian model selection but is computationally difficult. I argue that the marginal likelihood can be reli...
The following electromagnetism (EM) inverse problem is addressed. It consists in estimating local radioelectric properties of materials recovering an object from the global EM scattering measurement, ...
Abstract: Let $\mathscr{P}(E)$ be the space of probability measures on a measurable space $(E,\mathcal{E})$. In this paper we introduce a class of nonlinear Markov chain Monte Carlo (MCMC) methods for...
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...
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...
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 ...
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...

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