Zero variance differential geometric Markov chain Monte Carlo algorithms

DOI10.1214/13-BA848zbMath1327.60140OpenAlexW1998986742WikidataQ115240861 ScholiaQ115240861MaRDI QIDQ899008

Theodore Papamarkou, Antonietta Mira, Mark A. Girolami

Publication date: 21 December 2015

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ba/1393251772

zbMATH Keywords

Metropolis-Hastingscontrol variatesHamiltonian Monte CarloMetropolis adjusted Langevin algorithms

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Software, source code, etc. for problems pertaining to statistics (62-04) Geometric probability and stochastic geometry (60D05) Bayesian inference (62F15) General nonlinear regression (62J02)

Related Items (10)

Variance reduction for Markov chains with application to MCMC ⋮ Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization ⋮ Mobility Estimation for Langevin Dynamics Using Control Variates ⋮ Exploiting multi-core architectures for reduced-variance estimation with intractable likelihoods ⋮ Talking across fields: a physicist's presentation of some mathematical aspects of quantum Monte Carlo methods ⋮ Asymptotic variance for random walk Metropolis chains in high dimensions: logarithmic growth via the Poisson equation ⋮ Correlations between random projections and the bivariate normal ⋮ MCMC and the fibonacci distribution ⋮ Control variates for stochastic gradient MCMC ⋮ Variance reduction for Metropolis-Hastings samplers

Uses Software

• Matlab
• TFad
• BayesDA

Cites Work

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