منابع مشابه
Adaptive Multiple Importance Sampling
The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling scheme. The difference with earlier adaptive importance sampling implementations like Population Monte Carlo is that the importance weights of all simulated values, past as well as present, are recomputed at each iteration, following the technique of...
متن کاملMultiple importance sampling revisited: breaking the bounds
We revisit the multiple importance sampling (MIS) estimator and investigate the bound on the efficiency improvement over balance heuristic estimator with equal count of samples established in Veach’s thesis. We revise the proof for this and come to the conclusion that there is no such bound and henceforth it makes sense to look for new estimators that improve on balance heuristic estimator with...
متن کاملImportance Sampling for Reinforcement Learning with Multiple
This thesis considers three complications that arise from applying reinforcement learning to a real-world application. In the process of using reinforcement learning to build an adaptive electronic market-maker, we find the sparsity of data, the partial observability of the domain, and the multiple objectives of the agent to cause serious problems for existing reinforcement learning algorithms....
متن کاملHonest Importance Sampling with Multiple Markov Chains.
Importance sampling is a classical Monte Carlo technique in which a random sample from one probability density, π1, is used to estimate an expectation with respect to another, π. The importance sampling estimator is strongly consistent and, as long as two simple moment conditions are satisfied, it obeys a central limit theorem (CLT). Moreover, there is a simple consistent estimator for the asym...
متن کاملA R Adaptive Multiple Importance Sampling (ARAMIS)
ARAMIS is an R package that runs the AMIS [1] algorithm. The main features of ARAMIS are parallelization and customization. ARAMIS exploits the massively parallel structure of AMIS to improve the performance of the algorithm as it was implemented in the original paper. As a result simulation time is reduced by orders of magnitudes. As for customization, the potential of the R language is fully ...
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ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2016
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2016.2600678