THESIS 7967A Markov chain Monte Carlo (MCMC) algorithm is proposed for the evaluation of a posterior distribution. The posterior distribution is from a model that has a spatial structure and exhibits many characterisics which are typically cumbersome to MCMC algorithms. The algorithm is construct with the purpose of conquering or significantly reducing these difficulties. The performance of this algorithm is then investigated for a diversity of circumstances
Abstract. Many Bayesian inference problems require exploring the posterior distribution of high-dime...
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 201...
In case of a non-linear relation linking the model to the data, numerical Markov Chain Monte Carlo (...
The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of mode...
The marked increase in popularity of Bayesian methods in statistical practice over the last decade o...
International audienceComplex hierarchical models lead to a complicated likelihood and then, in a Ba...
Markov chain Monte Carlo (MCMC) methods have been used extensively in statistical physics over the l...
a b s t r a c t We present an overview of Markov chain Monte Carlo, a sampling method for model infe...
We study Markov chain Monte Carlo (MCMC) algorithms for target distributions defined on matrix space...
We investigate how ideas from covariance localization in numerical weather prediction can be used in...
Many Bayesian inference problems require exploring the posterior distribution of high-dimensional pa...
We propose new Markov Chain Monte Carlo algorithms to sample probability distributions on submanifol...
International audienceWe present an overview of Markov chain Monte Carlo, a sampling method for mode...
Markov Chain Monte Carlo (MCMC) methods are fundamental tools for sampling highly complex distributi...
This thesis addresses several issues appearing in Bayesian statistics. Firstly, computations for app...
Abstract. Many Bayesian inference problems require exploring the posterior distribution of high-dime...
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 201...
In case of a non-linear relation linking the model to the data, numerical Markov Chain Monte Carlo (...
The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of mode...
The marked increase in popularity of Bayesian methods in statistical practice over the last decade o...
International audienceComplex hierarchical models lead to a complicated likelihood and then, in a Ba...
Markov chain Monte Carlo (MCMC) methods have been used extensively in statistical physics over the l...
a b s t r a c t We present an overview of Markov chain Monte Carlo, a sampling method for model infe...
We study Markov chain Monte Carlo (MCMC) algorithms for target distributions defined on matrix space...
We investigate how ideas from covariance localization in numerical weather prediction can be used in...
Many Bayesian inference problems require exploring the posterior distribution of high-dimensional pa...
We propose new Markov Chain Monte Carlo algorithms to sample probability distributions on submanifol...
International audienceWe present an overview of Markov chain Monte Carlo, a sampling method for mode...
Markov Chain Monte Carlo (MCMC) methods are fundamental tools for sampling highly complex distributi...
This thesis addresses several issues appearing in Bayesian statistics. Firstly, computations for app...
Abstract. Many Bayesian inference problems require exploring the posterior distribution of high-dime...
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 201...
In case of a non-linear relation linking the model to the data, numerical Markov Chain Monte Carlo (...