Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization
From MaRDI portal
Publication:6154293
DOI10.1007/978-3-030-98319-2_10arXiv2006.07487OpenAlexW3034678031MaRDI QIDQ6154293
Chris J. Oates, Andrew B. Duncan, François-Xavier Briol, Shijing Si, Lawrence Carin
Publication date: 14 February 2024
Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2006.07487
Numerical mathematical programming methods (65K05) Monte Carlo methods (65C05) Stochastic programming (90C15)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Zero variance Markov chain Monte Carlo for Bayesian estimators
- Parametric Stein operators and variance bounds
- Fundamentals of Stein's method
- Exploiting multi-core architectures for reduced-variance estimation with intractable likelihoods
- Zero variance differential geometric Markov chain Monte Carlo algorithms
- Bayes-Hermite quadrature
- Variance reduction in Monte Carlo estimators via empirical variance minimization
- Convergence rates for a class of estimators based on Stein's method
- On the Poisson equation and diffusion approximation. I
- Variance reduction for Markov chains with application to MCMC
- Measuring sample quality with diffusions
- Probabilistic integration: a role in statistical computation?
- Control variates for stochastic gradient MCMC
- Control variates for quasi-Monte Carlo (with comments and rejoinder)
- Bayesian Calibration of Computer Models
- Variance Reduction for Simulated Diffusions
- Control Variates for Estimation Based on Reversible Markov Chain Monte Carlo Samplers
- Variance reduction through smoothing and control variates for Markov chain simulations
- Monte Carlo integration with a growing number of control variates
- Control Variates for the Metropolis–Hastings Algorithm
- Approximating Martingales for Variance Reduction in Markov Process Simulation
- Control Functionals for Monte Carlo Integration
This page was built for publication: Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization