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Stein $\Pi$-Importance Sampling
Advances in Neural Information Processing Systems 36, 2023 Stein discrepancies have emerged as a powerful tool for retrospective improvement of Markov chain Monte Carlo output. However, the question of how to design Markov chains that are well-suited to such post-processing has yet to be addressed. This paper studies Stein importance sampling, in which weights are assigned to the states visited by a $Π ...
Congye Wang +3 more
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Layered adaptive importance sampling [PDF]
Statistics and Computing, 2016 Monte Carlo methods represent the "de facto" standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples.
Luca Martino +3 more
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How Important is Importance Sampling for Deep Budgeted Training? [PDF]
Proceedings of the British Machine Vision Conference 2021, 2021 British Machine Vision Conference (BMVC) 2021, oral ...
Eric, Arazo +4 more
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Hyperdynamics Importance Sampling [PDF]
, 2002 Sequential random sampling (‘Markov Chain Monte-Carlo') is a popular strategy for many vision problems involving multimodal distributions over high-dimensional parameter spaces. It applies both to importance sampling (where one wants to sample points according to their ‘importance' for some calculation, but otherwise fairly) and to global optimization (
Sminchisescu, Cristian, Triggs, Bill
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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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CoRR, 2012 The paper introduces AND/OR importance sampling for probabilistic graphical models. In contrast to importance sampling, AND/OR importance sampling caches samples in the AND/OR space and then extracts a new sample mean from the stored samples. We prove that AND/OR importance sampling may have lower variance than importance sampling; thereby providing a ...
Vibhav Gogate, Rina Dechter
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Importance Sampling for Minibatches
J. Mach. Learn. Res., 2016 Minibatching is a very well studied and highly popular technique in supervised learning, used by practitioners due to its ability to accelerate training through better utilization of parallel processing power and reduction of stochastic variance. Another popular technique is importance sampling -- a strategy for preferential sampling of more important ...
Csiba, Dominik, Richtárik, Peter
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The Importance of Microhabitat for Biodiversity Sampling [PDF]
PLoS ONE, 2014 Responses to microhabitat are often neglected when ecologists sample animal indicator groups. Microhabitats may be particularly influential in non-passive biodiversity sampling methods, such as baited traps or light traps, and for certain taxonomic groups which respond to fine scale environmental variation, such as insects.
Mehrabi, Zia +3 more
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Dual Free Adaptive Minibatch SDCA for Empirical Risk Minimization
Frontiers in Applied Mathematics and Statistics, 2018 In this paper we develop an adaptive dual free Stochastic Dual Coordinate Ascent (adfSDCA) algorithm for regularized empirical risk minimization problems. This is motivated by the recent work on dual free SDCA of Shalev-Shwartz [1].
Xi He, Rachael Tappenden, Martin Takáč
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Heretical Multiple Importance Sampling [PDF]
IEEE Signal Processing Letters, 2016 Multiple Importance Sampling (MIS) methods approximate moments of complicated distributions by drawing samples from a set of proposal distributions. Several ways to compute the importance weights assigned to each sample have been recently proposed, with the so-called deterministic mixture (DM) weights providing the best performance in terms of variance,
Victor Elvira +3 more
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