We develop a sampling algorithm to explore the probability densities arising in Bayesian data analysis problems. Our algorithm is a multiparameter generalization of a replica-exchange Monte Carlo scheme. The strategy relies on gradual weighing of experimental data and on Tsallis generalized statistics. We demonstrate the effectiveness of the method on nuclear magnetic resonance data for a folded protein
AbstractNested sampling is a Bayesian sampling technique developed to explore probability distributi...
This paper presents an approach to enhance conformational sampling of proteins employing stochastic ...
Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a probability distribution, via a...
We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that co...
The Boltzmann distribution is commonly used as a prior probability in Bayesian data analysis. Exampl...
We discuss several algorithms for sampling from unnormalized probability distributions in statistica...
Bayesian inference often requires integrating some function with respect to a posterior distribution...
Monte Carlo (MC) algorithm aims to generate samples from a given probability distribution P (X) with...
The Metropolis-Hastings (MH) algorithm of Hastings (1970) is a Markov chain Monte Carlo method that ...
International audienceWe propose a new algorithm for sampling the N-body density mid R:Psi(R)mid R:(...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample...
Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods have emerged as the two mai...
In recent years, dynamical modelling has been provided with a range of breakthrough methods to perfo...
We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Baye...
AbstractNested sampling is a Bayesian sampling technique developed to explore probability distributi...
This paper presents an approach to enhance conformational sampling of proteins employing stochastic ...
Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a probability distribution, via a...
We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that co...
The Boltzmann distribution is commonly used as a prior probability in Bayesian data analysis. Exampl...
We discuss several algorithms for sampling from unnormalized probability distributions in statistica...
Bayesian inference often requires integrating some function with respect to a posterior distribution...
Monte Carlo (MC) algorithm aims to generate samples from a given probability distribution P (X) with...
The Metropolis-Hastings (MH) algorithm of Hastings (1970) is a Markov chain Monte Carlo method that ...
International audienceWe propose a new algorithm for sampling the N-body density mid R:Psi(R)mid R:(...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample...
Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods have emerged as the two mai...
In recent years, dynamical modelling has been provided with a range of breakthrough methods to perfo...
We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Baye...
AbstractNested sampling is a Bayesian sampling technique developed to explore probability distributi...
This paper presents an approach to enhance conformational sampling of proteins employing stochastic ...
Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a probability distribution, via a...