Unbiased Markov chain Monte Carlo methods with couplings
Summary Markov chain Monte Carlo (MCMC) methods provide consistent approximations of
integrals as the number of iterations goes to∞. MCMC estimators are generally biased after …
integrals as the number of iterations goes to∞. MCMC estimators are generally biased after …
Computed tomography and magnetic resonance imaging are potential noninvasive methods for evaluating the cisterna chyli in cats
NG Martín, ED Miño - Journal of the American Veterinary …, 2024 - Am Vet Med Assoc
OBJECTIVE There is limited information on the normal appearance of the cisterna chyli (CC)
in cats on CT and MRI. The aim of this retrospective study was to describe the CT and MRI …
in cats on CT and MRI. The aim of this retrospective study was to describe the CT and MRI …
The Hastings algorithm at fifty
In a 1970 Biometrika paper, WK Hastings developed a broad class of Markov chain
algorithms for sampling from probability distributions that are difficult to sample from directly …
algorithms for sampling from probability distributions that are difficult to sample from directly …
Unbiased Multilevel Monte Carlo methods for intractable distributions: MLMC meets MCMC
Constructing unbiased estimators from Markov chain Monte Carlo (MCMC) outputs is a
difficult problem that has recently received a lot of attention in the statistics and machine …
difficult problem that has recently received a lot of attention in the statistics and machine …
Estimating convergence of Markov chains with L-lag couplings
Abstract Markov chain Monte Carlo (MCMC) methods generate samples that are
asymptotically distributed from a target distribution of interest as the number of iterations …
asymptotically distributed from a target distribution of interest as the number of iterations …
Maximal couplings of the metropolis-hastings algorithm
Couplings play a central role in the analysis of Markov chain Monte Carlo algorithms and
appear increasingly often in the algorithms themselves, eg in convergence diagnostics …
appear increasingly often in the algorithms themselves, eg in convergence diagnostics …
Unbiased markov chain monte carlo with couplings
Markov chain Monte Carlo (MCMC) methods provide consistent of integrals as the number
of iterations goes to infinity. MCMC estimators are generally biased after any fixed number of …
of iterations goes to infinity. MCMC estimators are generally biased after any fixed number of …
Computing Bayes: Bayesian computation from 1763 to the 21st century
The Bayesian statistical paradigm uses the language of probability to express uncertainty
about the phenomena that generate observed data. Probability distributions thus …
about the phenomena that generate observed data. Probability distributions thus …
Solving the Poisson equation using coupled Markov chains
This article shows how coupled Markov chains that meet exactly after a random number of
iterations can be used to generate unbiased estimators of the solutions of the Poisson …
iterations can be used to generate unbiased estimators of the solutions of the Poisson …
Debiasing piecewise deterministic Markov process samplers using couplings
Monte Carlo methods-such as Markov chain Monte Carlo (MCMC) and piecewise
deterministic Markov process (PDMP) samplers-provide asymptotically exact estimators of …
deterministic Markov process (PDMP) samplers-provide asymptotically exact estimators of …