Aug 24, 2011 · In this paper we present Stacked Monte Carlo (StackMC), a new method for post-processing an existing set of MC samples to improve the associated ...
Monte Carlo techniques are used to estimate the integrals of a function using randomly generated samples. The interest in uncertainty quantification and ...
This paper describes such a technique that combines MC with machine learning and statistical techniques that leads to significant computational savings over tra ...
Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function.
Stacked Monte Carlo is presented, which is a new method for postprocessing an existing set of Monte Carlo samples to improve integral estimation, ...
Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function.
Jul 18, 2016 · Yes. Unlike what other answers state, 'typical' machine-learning methods such as nonparametrics and (deep) neural networks can help create better MCMC samplers.
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Oct 1, 2023 · Importance sampling means using a substitute density q(⋅) when integrating an arbitrary integrable function H(⋅) ∫H(y)dy.
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Using Supervised Learning to Improve Monte Carlo Integral Estimation. Tracey, B.; Wolpert, D.; Alonso, J.J.. The Aiaa/Asme/ASCE/AHS/ASC Structures, Structural ...
We will generally seek to rewrite such gradients in a form that allows for Monte Carlo estimation, allowing them to be easily and efficiently used and analysed.