We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing different proposal distributions in the multipletry generation step. We exploit the structure of the MTM algorithm with different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods and builds connections with the rapidly expanding world of adaptive MCMC. We show the validity of the algorithm and discuss the choice of the selection weights and of the...
International audienceIn many situations it is important to be able to propose N independent realiza...
International audienceIn many situations it is important to be able to propose N independent realiza...
International audienceIn many situations it is important to be able to propose N independent realiza...
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increa...
We introduce a new class of interacting Markov chain Monte Carlo (MCMC) algorithms which is designed...
We introduce a new class of interacting Markov chain Monte Carlo (MCMC) algorithms which is designed...
We introduce a new class of interacting Markov chain Monte Carlo (MCMC) algorithms which is designed...
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increa...
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increa...
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increa...
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increa...
The multiple proposal methods represent a recent simulation technique for Markov Chain Monte Carlo t...
The multiple proposal methods represent a recent simulation technique for Markov Chain Monte Carlo t...
AbstractMultiple-try methods are extensions of the Metropolis algorithm in which the next state of t...
In many situations it is important to be able to propose N independent realizations of a given distr...
International audienceIn many situations it is important to be able to propose N independent realiza...
International audienceIn many situations it is important to be able to propose N independent realiza...
International audienceIn many situations it is important to be able to propose N independent realiza...
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increa...
We introduce a new class of interacting Markov chain Monte Carlo (MCMC) algorithms which is designed...
We introduce a new class of interacting Markov chain Monte Carlo (MCMC) algorithms which is designed...
We introduce a new class of interacting Markov chain Monte Carlo (MCMC) algorithms which is designed...
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increa...
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increa...
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increa...
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increa...
The multiple proposal methods represent a recent simulation technique for Markov Chain Monte Carlo t...
The multiple proposal methods represent a recent simulation technique for Markov Chain Monte Carlo t...
AbstractMultiple-try methods are extensions of the Metropolis algorithm in which the next state of t...
In many situations it is important to be able to propose N independent realizations of a given distr...
International audienceIn many situations it is important to be able to propose N independent realiza...
International audienceIn many situations it is important to be able to propose N independent realiza...
International audienceIn many situations it is important to be able to propose N independent realiza...