Corpus ID: 53342004

Control Variates for Reversible MCMC Samplers

@article{Dellaportas2010ControlVF,
  title={Control Variates for Reversible MCMC Samplers},
  author={Petros Dellaportas and Ioannis Kontoyiannis},
  journal={arXiv: Computation},
  year={2010}
}
A general methodology is introduced for the construction and effective application of control variates to estimation problems involving data from reversible MCMC samplers. We propose the use of a specific class of functions as control variates, and we introduce a new, consistent estimator for the values of the coefficients of the optimal linear combination of these functions. The form and proposed construction of the control variates is derived from our solution of the Poisson equation… Expand

Figures and Tables from this paper

Paper Mentions

Blog Post
Control Variates for Estimation Based on Reversible MCMC Samplers
A general methodology is introduced for the construction and effective application of control variates to estimation problems involving data from reversible MCMC samplers. We propose the use of aExpand
Control variates for estimation based on reversible Markov chain Monte Carlo samplers
Summary. A general methodology is introduced for the construction and effective application of control variates to estimation problems involving data from reversible Markov chain Monte CarloExpand
Zero variance Markov chain Monte Carlo for Bayesian estimators
TLDR
A general purpose variance reduction technique for the MCMC estimator, based on the zero-variance principle introduced in the physics literature, is proposed, and conditions for asymptotic unbiasedness of the Zero Variance estimator are derived. Expand
Riemann manifold Langevin and Hamiltonian Monte Carlo methods
The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined on the Riemann manifold to resolve the shortcomings of existing Monte Carlo algorithms whenExpand
Density estimators through Zero Variance Markov Chain Monte Carlo
A Markov Chain Monte Carlo method is proposed for the pointwise evaluation of a density whose normalizing constant is not known. This method was introduced in the physics literature by Assaraf et alExpand

References

SHOWING 1-10 OF 80 REFERENCES
Control Variates for Estimation Based on Reversible MCMC Samplers
A general methodology is introduced for the construction and effective application of control variates to estimation problems involving data from reversible MCMC samplers. We propose the use of aExpand
Notes on Using Control Variates for Estimation with Reversible MCMC Samplers
A general methodology is presented for the construction and effective use of control variates for reversible MCMC samplers. The values of the coefficients ofthe optimal linear combination of theExpand
Control Variates for the Metropolis–Hastings Algorithm
We propose new control variates for variance reduction in estimation of mean values using the Metropolis-Hastings algorithm. Traditionally, states that are rejected in the Metropolis-HastingsExpand
Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling
TLDR
The solutions to the label switching problem of Markov chain Monte Carlo methods, such as artificial identifiability constraints, relabelling algorithms and label invariant loss functions are reviewed. Expand
Space-varying regression models: specifications and simulation
Space-varying regression models are generalizations of standard linear models where the regression coefficients are allowed to change in space. The spatial structure is specified by a multivariateExpand
MCMC and the Label Switching Problem in Bayesian Mixture Modelling 1 Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modelling
In the past ten years there has been a dramatic increase of interest in the Bayesian analysis of finite mixture models. This is primarily because of the emergence of Markov chain Monte Carlo (MCMC)Expand
Metropolis Methods, Gaussian Proposals and Antithetic Variables
We investigate various aspects of a class of dynamic Monte Carlo methods, that generalises the Metropolis algorithm and includes the Gibbs sampler as a special case. These can be used to estimateExpand
Gibbs sampling for Bayesian non‐conjugate and hierarchical models by using auxiliary variables
We demonstrate the use of auxiliary (or latent) variables for sampling non‐standard densities which arise in the context of the Bayesian analysis of non‐conjugate and hierarchical models by using aExpand
Alternatives to the Gibbs Sampling Scheme
A variation of the Gibbs sampling scheme is deened by driving the simulated Markov chain by the conditional distributions of an approximation to the posterior rather than the posterior distributionExpand
Markov Chains for Exploring Posterior Distributions
Several Markov chain methods are available for sampling from a posterior distribution. Two important examples are the Gibbs sampler and the Metropolis algorithm. In addition, several strategies areExpand
...
1
2
3
4
5
...