Langevin-type models. I: Diffusions with given stationary distributions and their discretizations
From MaRDI portal
Publication:1961832
DOI10.1023/A:1010086427957zbMath0947.60071MaRDI QIDQ1961832
Osnat Stramer, Richard L. Tweedie
Publication date: 1 November 2000
Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)
Monte CarloMarkov chaindiffusionsposterior distributionsuniform ergodicityexponential ergodicityEuler schemesirreducible Markov processesLangevin models
Discrete-time Markov processes on general state spaces (60J05) Queueing theory (aspects of probability theory) (60K25) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10)
Related Items
Stochastic zeroth-order discretizations of Langevin diffusions for Bayesian inference, Proximal Markov chain Monte Carlo algorithms, On the computational complexity of MCMC-based estimators in large samples, Scaling Limits for the Transient Phase of Local Metropolis–Hastings Algorithms, Convergent stochastic expectation maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation, Ergodicity and exponential \(\beta\)-mixing bounds for multidimensional diffusions with jumps, On sampling from a log-concave density using kinetic Langevin diffusions, Unnamed Item, Metropolis-Hastings algorithms with acceptance ratios of nearly 1, Strong invariance principles for ergodic Markov processes, Weak local linear discretizations for stochastic differential equations: convergence and numerical schemes, Unnamed Item, Convergence rate of weak local linearization schemes for stochastic differential equations with additive noise, Stationarity preserving and efficiency increasing probability mass transfers made possible, f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models, Subgeometric ergodicity of strong Markov processes, A note on convergence rate of a linearization method for the discretization of stochastic differential equations, User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient, Bayesian computation: a summary of the current state, and samples backwards and forwards, Non-asymptotic guarantees for sampling by stochastic gradient descent, Langevin type limiting processes for adaptive MCMC, Subgeometric rates of convergence of \(f\)-ergodic strong Markov processes, Ergodicity of the zigzag process, \(V\)-subgeometric ergodicity for a Hastings-Metropolis algorithm, Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise., Polynomial ergodicity of Markov transition kernels.
