The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method
نویسندگان
چکیده
منابع مشابه
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
Many Markov chain Monte Carlo techniques currently available rely on discrete-time re-versible Markov processes whose transition kernels are variations of the Metropolis–Hastingsalgorithm. We explore and generalize an alternative scheme recently introduced in the physicsliterature [27] where the target distribution is explored using a continuous-time non-reversiblepiecewise-...
متن کاملAnalysis of a Nonreversible Markov Chain Sampler
We analyze the convergence to stationarity of a simple nonreversible Markov chain that serves as a model for several nonreversible Markov chain sampling methods that are used in practice. Our theoretical and numerical results show that nonreversibility can indeed lead to improvements over the diffusive behavior of simple Markov chain sampling schemes. The analysis uses both probabilistic techni...
متن کاملParticle Markov Chain Monte Carlo
Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods have emerged as the two main tools to sample from high-dimensional probability distributions. Although asymptotic convergence of MCMC algorithms is ensured under weak assumptions, the performance of these latters is unreliable when the proposal distributions used to explore the space are poorly chosen and/or if highly corr...
متن کاملStochastic Bouncy Particle Sampler
We introduce a stochastic version of the nonreversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear, to efficiently sample Bayesian posteriors in big datasets. We prove that in the BPS no bias is introduced by noisy evaluations of the log-likelihood gradient. On the other hand, we argue that efficiency considerations favor a smal...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2018
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2017.1294075