Results 21 to 30 of about 2,663,721 (268)
Assessing Asset-Liability Risk with Neural Networks
Risks, 2020 We introduce a neural network approach for assessing the risk of a portfolio of assets and liabilities over a given time period. This requires a conditional valuation of the portfolio given the state of the world at a later time, a problem that is ...
Patrick Cheridito +2 more
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Estimating Tail Probabilities of Random Sums of Phase-Type Scale Mixture Random Variables
Algorithms, 2022 We consider the problem of estimating tail probabilities of random sums of scale mixture of phase-type distributions—a class of distributions corresponding to random variables which can be represented as a product of a non-negative but otherwise ...
Hui Yao, Thomas Taimre
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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 ...
Llorente, Fernando +3 more
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Automatic Tempered Posterior Distributions for Bayesian Inversion Problems
Mathematics, 2021 We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise are carried out using distinct (but interacting) methods.
Luca Martino +4 more
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Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM
Econometrics, 2016 This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures of ...
Nalan Baştürk +3 more
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Importance Sampling for Dispersion-managed Solitons [PDF]
, 2010 The dispersion-managed nonlinear Schrödinger (DMNLS) equation governs the long-term dynamics of systems which are subject to large and rapid dispersion variations.
Spiller, Elaine T., Biondini, Gino
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Importance is Important: Generalized Markov Chain Importance Sampling Methods
, 2023 We show that for any multiple-try Metropolis algorithm, one can always accept the proposal and evaluate the importance weight that is needed to correct for the bias without extra computational cost. This results in a general, convenient, and rejection-free Markov chain Monte Carlo (MCMC) sampling scheme.
Li, Guanxun, Smith, Aaron, Zhou, Quan
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Multifidelity importance sampling [PDF]
Computer Methods in Applied Mechanics and Engineering, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peherstorfer, Benjamin +3 more
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Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation
Frontiers in Big Data, 2022 Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists.
Shenggang Hu +11 more
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, 2023 Fixed a few typos and errors, and added a real data ...
Datta, Jyotishka, Polson, Nicholas G.
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