Add to ORKGA general methodology is presented for the construction and effective use of control variates for reversible MCMC samplers. The values of the coefficients of the optimal linear combination of the control variates are computed, and adaptive, consistent MCMC estimators are derived for these optimal coefficients. All methodological and asymptotic arguments are rigorously justified. Numerous MCMC simulation examples from Bayesian inference applications demonstrate that the resulting variance reduction can be quite dramatic
A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outc...
A solution is offered to the general problem of optimal selection of control variates. Solutions are...
We describe adaptive Markov chain Monte Carlo (MCMC) methods for sampling posterior distributions ar...
A general methodology is presented for the construction and effective use of control variates for re...
A general methodology is introduced for the construction and effective application of control variat...
A general methodology is introduced for the construction and effective application of control variat...
A new methodology is presented for the construction of control variates to reduce the variance of ad...
Abstract. The method of control variates is one of the most widely used variance reduction technique...
This paper provides a unified development of the method of control variates for simulation experimen...
The use of control variates is a well-known variance reduction tech- nique in Monte Carlo integratio...
In the present thesis we are concerned with appropriate variance reduction methods for specific clas...
I describe algorithms for drawing from distributions using adaptive Markov chain Monte Carlo (MCMC) ...
We present in this paper a multiple change-point analysis for which an MCMC sampler plays a fundamen...
The technique of control variates requires that the user iden-tify a set of variates that are correl...
Introduction When fitting an autoregressive model to Gaussian time series data, often the correct o...
A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outc...
A solution is offered to the general problem of optimal selection of control variates. Solutions are...
We describe adaptive Markov chain Monte Carlo (MCMC) methods for sampling posterior distributions ar...
A general methodology is presented for the construction and effective use of control variates for re...
A general methodology is introduced for the construction and effective application of control variat...
A general methodology is introduced for the construction and effective application of control variat...
A new methodology is presented for the construction of control variates to reduce the variance of ad...
Abstract. The method of control variates is one of the most widely used variance reduction technique...
This paper provides a unified development of the method of control variates for simulation experimen...
The use of control variates is a well-known variance reduction tech- nique in Monte Carlo integratio...
In the present thesis we are concerned with appropriate variance reduction methods for specific clas...
I describe algorithms for drawing from distributions using adaptive Markov chain Monte Carlo (MCMC) ...
We present in this paper a multiple change-point analysis for which an MCMC sampler plays a fundamen...
The technique of control variates requires that the user iden-tify a set of variates that are correl...
Introduction When fitting an autoregressive model to Gaussian time series data, often the correct o...
A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outc...
A solution is offered to the general problem of optimal selection of control variates. Solutions are...
We describe adaptive Markov chain Monte Carlo (MCMC) methods for sampling posterior distributions ar...