An Extension of Peskun and Tierney Orderings to Continuous Time Markov Chains
نویسندگان
چکیده
Peskun ordering is a partial ordering defined on the space of transition matrices of discrete time Markov chains. If the Markov chains are reversible with respect to a common stationary distribution π, Peskun ordering implies an ordering on the asymptotic variances of the resulting Markov chain Monte Carlo estimators of integrals with respect to π. Peskun ordering is also relevant in the framework of time-invariant estimating equations in that it provides a necessary condition for ordering the asymptotic variances of the resulting estimators. Tierney ordering extends Peskun ordering from finite to general state spaces. In this paper Peskun and Tierney orderings are extended from discrete time to continuous time Markov chains.
منابع مشابه
Minimising Mcmc Variance via Diffusion Limits, with an Application to Simulated Tempering
We derive new results comparing the asymptotic variance of diffusions by writing them as appropriate limits of discrete-time birth–death chains which themselves satisfy Peskun orderings. We then apply our results to simulated tempering algorithms to establish which choice of inverse temperatures minimises the asymptotic variance of all functionals and thus leads to the most efficient MCMC algor...
متن کاملStochastic Dynamic Programming with Markov Chains for Optimal Sustainable Control of the Forest Sector with Continuous Cover Forestry
We present a stochastic dynamic programming approach with Markov chains for optimal control of the forest sector. The forest is managed via continuous cover forestry and the complete system is sustainable. Forest industry production, logistic solutions and harvest levels are optimized based on the sequentially revealed states of the markets. Adaptive full system optimization is necessary for co...
متن کاملOn the use of auxiliary variables in Markov chain Monte Carlo sampling
We study the slice sampler, a method of constructing a reversible Markov chain with a speciied invariant distribution. Given an independence Metropolis-Hastings algorithm it is always possible to construct a slice sampler that dominates it in the Peskun sense. This means that the resulting Markov chain produces estimates with a smaller asymptotic variance. Furthermore the slice sampler has a sm...
متن کاملOn the choice of the stochastic comparison method for multidimensional Markov chains analysis
The ≼Φ stochastic comparison (≼Φ∈ {≼st, ≼wk, ≼wk∗}) of multidimensional Continuous Time Markov Chains (CTMC)s is an efficient but a complex method for the performability evaluation of computer systems. Different techniques can be applied for the stochastic comparison of Markov chains. The coupling is an intuitive method, and may be applied by comparing the evolution of sample paths due to event...
متن کاملOrdering Monte Carlo Markov Chains
Markov chains having the same stationary distribution can be partially ordered by performance in the central limit theorem. We say that one chain is at least as good as another in the e ciency partial ordering if the variance in the central limit theorem is at least as small for every L( ) functional of the chain. Peskun partial ordering implies e ciency partial ordering [25, 30]. Here we show ...
متن کامل