جهت دسترسی به کاربرگه ی زیر، از این لینک استفاده کنید. http://dl.pgu.ac.ir/handle/Hannan/66371
Title: Convergence of Monte Carlo distribution estimates from rival samplers
Keywords: Science & Technology;Technology;Physical Sciences;Computer Science, Theory & Methods;Statistics & Probability;Computer Science;Mathematics;Sample sizes;Jensen-Shannon divergence;Transdimensional Markov chains;MARKOV-CHAINS;ENTROPY;Statistics & Probability;0104 Statistics;0802 Computation Theory And Mathematics
Issue Date: 8-Jul-2015
Publisher: Springer Verlag
Description: It is often necessary to make sampling-based statistical inference about many probability distributions in parallel. Given a finite computational resource, this article addresses how to optimally divide sampling effort between the samplers of the different distributions. Formally approaching this decision problem requires both the specification of an error criterion to assess how well each group of samples represent their underlying distribution, and a loss function to combine the errors into an overall performance score. For the first part, a new Monte Carlo divergence error criterion based on Jensen–Shannon divergence is proposed. Using results from information theory, approximations are derived for estimating this criterion for each target based on a single run, enabling adaptive sample size choices to be made during sampling.
URI: https://dx.doi.org/10.1007/s11222-015-9595-0
Other Identifiers: 1573-1375
http://hdl.handle.net/10044/1/32940
Type Of Material: OTHER
Appears in Collections:Mathematics

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