• Aucun résultat trouvé

GEOMETRIC ERGODICITY OF THE BOUNCY PARTICLE SAMPLER

N/A
N/A
Protected

Academic year: 2021

Partager "GEOMETRIC ERGODICITY OF THE BOUNCY PARTICLE SAMPLER"

Copied!
35
0
0

Texte intégral

Loading

Figure

Figure 1. Before the first refreshment at time S 1 , both processes may bounce freely

Références

Documents relatifs

– MCMC yields Up component velocity uncertainties which are around [1.7,4.6] times larger than from CATS, and within the millimetre per year range

A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space Eric Heitz Unity Technologies Laurent Belcour Unity Technologies V.. Lyon, CNRS

length, when the Gibbs sampler is implemented by blocking the correlated parameters and sampling from the respective conditional posterior distributions takes place in

Probabilistic infer- ences using Markov chain Monte Carlo (MCMC) methods are considered as another Monte Carlo simulation technique, and other measurand pdf estimation alternatives,

If the confidence region is a widely used approach to provide the uncertainty associated with a 2-D function in the statistics field, the aerosol community generally expresses the

Key-words: Feynman–Kac flow, nonnegative selection function, binary selection function, interacting particle system, extinction time, random number of particles, central limit

Unité de recherche INRIA Rennes, Irisa, Campus universitaire de Beaulieu, 35042 RENNES Cedex Unité de recherche INRIA Rhône-Alpes, 655, avenue de l’Europe, 38330 MONTBONNOT ST

Probabilities of errors for the single decoder: (solid) Probability of false negative, (dashed) Probability of false positive, [Down] Average number of caught colluders for the