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ACM Transactions on Graphics, 2020 We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration. So far, the core challenge of applying the method of control variates has been finding a good approximation of the integrand that is cheap to integrate.
Müller, Thomas +3 more
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Asymptotic expansions as control variates for deep solvers to fully-coupled forward-backward stochastic differential equations. [PDF]
PLoS ONECoupled forward-backward stochastic differential equations (FBSDEs) are closely related to financially important issues such as optimal investment. However, it is well known that obtaining solutions is challenging, even when employing numerical methods ...
Makoto Naito +3 more
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Implementation of variance reduction techniques applied to the pricing of investment certificates [PDF]
Risk Management Magazine, 2023 Certificates are structured financial instruments that aim to provide investors with investment solutions tailored to their needs. Certificates can be modeled using a bond component and a derivative component, typically an options strategy.
Anna Bottasso +3 more
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Modified control variates method based on second-order saddle-point approximation for practical reliability analysis [PDF]
Mechanical Sciences, 2023 A novel method is presented for efficiently analyzing the reliability of engineering components and systems with highly nonlinear complex limit state functions.
X. En, X. En, Y. Zhang, X. Huang
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Mitigating the noise of DESI mocks using analytic control variates
The Open Journal of Astrophysics, 2023 In order to address fundamental questions related to the expansion history of the Universe and its primordial nature with the next generation of galaxy experiments, we need to model reliably large-scale structure observables such as the correlation ...
Boryana Hadzhiyska +32 more
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Fast Compression of MCMC Output
Entropy, 2021 We propose cube thinning, a novel method for compressing the output of an MCMC (Markov chain Monte Carlo) algorithm when control variates are available. It allows resampling of the initial MCMC sample (according to weights derived from control variates),
Nicolas Chopin, Gabriel Ducrocq
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Barekeng, 2023 In this paper, we applied the standard Monte Carlo, antithetic variate, and control variates methods to value the double barrier knock-in option price.
Romaito Br Silalahi +2 more
doaj +1 more source
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders
Frontiers in Applied Mathematics and Statistics, 2022 Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble
Andrey A. Popov, Adrian Sandu
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ANALISIS RISIKO PORTOFOLIO MENGGUNAKAN METODE SIMULASI MONTE CARLO CONTROL VARIATES
E-Jurnal Matematika, 2021 Value at Risk (VaR) is a method to measure the maximum loss with a certain level of confidence in a certain period. Monte Carlo simulation is the most popular method of calculating VaR.
IRENE MAYLINDA PANGARIBUAN +2 more
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Discrete Variational Optimal Control [PDF]
Journal of Nonlinear Science, 2012 30 pages, 6 ...
Fernando Jiménez +2 more
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