منابع مشابه
2 5 Ju n 20 15 Markov Interacting Importance Samplers
We introduce a new Markov chain Monte Carlo (MCMC) sampler called the Markov Interacting Importance Sampler (MIIS). The MIIS sampler uses conditional importance sampling (IS) approximations to jointly sample the current state of the Markov Chain and estimate conditional expectations, possibly by incorporating a full range of variance reduction techniques. We compute Rao-Blackwellized estimates ...
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متن کاملComparing Markov Chain Samplers for Molecular Simulation
Markov chain Monte Carlo sampling propagators, including numerical integrators for stochastic dynamics, are central to the calculation of thermodynamic quantities and determination of structure for molecular systems. Efficiency is paramount, and to a great extent, this is determined by the integrated autocorrelation time (IAcT). This quantity varies depending on the observable that is being est...
متن کاملStratified Sampling with Spherically Symmetric Importance Samplers
SUMMARY Multivariate normal or Student importance sampling is a commonly used technique for integration problems in statistical inference. This integration approach is easy to implement, has straightforward error estimates and is eeective in a number of problems. A variety of variance reduction techniques can be considered with importance sampling. Stratiied sampling is one of these and in fact...
متن کاملImportance Analysis with Markov Chains
In order to maximize system dependability improvements we need criteria for placement of component redundancy. One such criterion is based on quantitative measures provided by importance theory. Importance coefficients of components in mathematical models provide numerical ranks based on the contribution of the component to a system event occurrence (i.e., the one for which the model was constr...
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ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2015
ISSN: 1556-5068
DOI: 10.2139/ssrn.2569488