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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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Particle Efficient Importance Sampling [PDF]
Journal of Econometrics, 2013 The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integrands to build variance minimising importance samplers. Despite a number of
Kohn, Robert, Scharth, Marcel
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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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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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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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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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Importance Nested Sampling and the MultiNest Algorithm
The Open Journal of Astrophysics, 2019 Bayesian inference involves two main computational challenges. First, in estimating the parameters of some model for the data, the posterior distribution may well be highly multi-modal: a regime in which the convergence to stationarity of traditional ...
Farhan Feroz +3 more
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Quantile estimation with adaptive importance sampling [PDF]
, 2010 We introduce new quantile estimators with adaptive importance sampling. The adaptive estimators are based on weighted samples that are neither independent nor identically distributed.
Egloff, Daniel, Leippold, Markus
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