Results 21 to 30 of about 2,693,397 (282)
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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Cut-off importance sampling of bole volume.
Silva Fennica, 1997 Cut-off importance sampling (CIS) is introduced as a means of sampling individual trees for the purpose of estimating bole volume. The novel feature of this variant of importance sampling is the establishment on the bole of a cut-off height, H, above ...
Robinson, Andrew +2 more
doaj +1 more source
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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Adaptive Importance Sampling in General Mixture Classes [PDF]
, 2008 In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the importance sampling performances, as measured by an entropy criterion.
A. Doucet +18 more
core +7 more sources
Rare event simulation via importance sampling for linear SPDE's [PDF]
, 2017 The goal of this paper is to develop provably efficient importance sampling Monte Carlo methods for the estimation of rare events within the class of linear stochastic partial differential equations (SPDEs).
Salins, Michael +1 more
core +3 more sources
Dynamic importance sampling for uniformly recurrent markov chains [PDF]
, 2005 Importance sampling is a variance reduction technique for efficient estimation of rare-event probabilities by Monte Carlo. In standard importance sampling schemes, the system is simulated using an a priori fixed change of measure suggested by a large ...
Dupuis, Paul, Wang, Hui
core +1 more source
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 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
openaire +2 more sources