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Generalizing the Balance Heuristic Estimator in Multiple Importance Sampling [PDF]
Entropy, 2022 In this paper, we propose a novel and generic family of multiple importance sampling estimators. We first revisit the celebrated balance heuristic estimator, a widely used Monte Carlo technique for the approximation of intractable integrals.
Mateu Sbert, Víctor Elvira
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Relative importance sampling for off-policy actor-critic in deep reinforcement learning [PDF]
Scientific ReportsOff-policy learning exhibits greater instability when compared to on-policy learning in reinforcement learning (RL). The difference in probability distribution between the target policy ( $$\pi$$ ) and the behavior policy (b) is a major cause of ...
Mahammad Humayoo +9 more
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Importance sampling for stochastic quantum simulations [PDF]
Quantum, 2023 Simulating many-body quantum systems is a promising task for quantum computers. However, the depth of most algorithms, such as product formulas, scales with the number of terms in the Hamiltonian, and can therefore be challenging to implement on near ...
Oriel Kiss +2 more
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Optimality in noisy importance sampling [PDF]
Signal Processing, 2022 In this work, we analyze the noisy importance sampling (IS), i.e., IS working with noisy evaluations of the target density. We present the general framework and derive optimal proposal densities for noisy IS estimators. The optimal proposals incorporate the information of the variance of the noisy realizations, proposing points in regions where the ...
Fernando Llorente 0001 +3 more
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MadNIS - Neural multi-channel importance sampling
SciPost Physics, 2023 Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling, to improve classical methods for ...
Theo Heimel, Ramon Winterhalder, Anja Butter, Joshua Isaacson, Claudius Krause, Fabio Maltoni, Olivier Mattelaer, Tilman Plehn
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An intuitive framework for Bayesian posterior simulation methods
Global Epidemiology, 2021 Purpose: Bayesian inference has become popular. It offers several pragmatic approaches to account for uncertainty in inference decision-making. Various estimation methods have been introduced to implement Bayesian methods.
Razieh Bidhendi Yarandi +3 more
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Implicitly adaptive importance sampling [PDF]
Statistics and Computing, 2021 AbstractAdaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability distributions whose parameters are adapted based on the mismatch between the current proposal and a target distribution.
Topi Paananen +3 more
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Importance Sampling for Time-Variant Reliability Analysis
IEEE Access, 2021 Importance sampling methods are extensively used in time-independent reliability analysis. However, the kind of methods is barely studied in the field of time-variant reliability analysis.
Jian Wang, Runan Cao, Zhili Sun
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Neural Importance Sampling [PDF]
ACM Transactions on Graphics, 2019 We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent components estimation (NICE), which we extend in numerous ways to improve performance and enable its application to integration problems.
Thomas Müller 0013 +4 more
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Importance nested sampling with normalising flows
Machine Learning: Science and Technology, 2023 We present an improved version of the nested sampling algorithm nessai in which the core algorithm is modified to use importance weights. In the modified algorithm, samples are drawn from a mixture of normalising flows and the requirement for samples to ...
Michael J Williams +2 more
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