This paper investigates the variance reduction techniques Antithetic Variates (AV) and Latin Hypercube Sampling (LHS) when used for sequential sampling in stochastic programming presents a comparative computational study. It shows conditions under which with AV LHS satisfy finite stopping guarantees are asymptotically valid, discussing detail. computationally compares their use both non-sequent...

In positron emission tomography (PET), random coincidence events must be removed from the measured signal in order to obtain quantitatively accurate data. The most widely implemented technique for estimating the number of random coincidences on a particular line of response is the delayed coincidence channel method. Estimates obtained in this way are subject to Poisson noise, which then propaga...

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 4 September 2019Accepted: 29 January 2021Published online: 13 April 2021Keywordscomposite stochastic optimization, proximal gradient method, variance reductionAMS Subject Headings68Q25, 68W20, 90C15, 90C26, 90C30Publication DataISSN (print): 1052-6234ISSN (online): 1095-7...

We study a variance reduction technique for Monte Carlo estimation of functionals in Markov chains. The method is based on designing sequential control variates using successive approximations of the function of interest V . Regular Monte Carlo estimates have a variance of O(1/N), where N is the number of sample trajectories of the Markov chain. Here, we obtain a geometric variance reduction O(...