Multilevel Delayed Acceptance MCMC with an Adaptive Error Model in PyMC3

Mikkel B. Lykkegaard, Grigorios Mingas, Robert Scheichl, Colin Fox, Tim J. Dodwell

Research output: Contribution to conferencePaperpeer-review

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Abstract

Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance when the evaluation it involves solving a Partial Differential Equation (PDE), as is the case in a wide range of engineering applications. Multilevel Delayed Acceptance (MLDA) with an Adaptive Error Model (AEM) is a novel approach, which alleviates this problem by exploiting a hierarchy of models, with increasing complexity and cost, and correcting the inexpensive models on-the-fly. The method has been integrated within the open-source probabilistic programming package PyMC3 and is available in the latest development version. In this paper, the algorithm is presented along with an illustrative example.
Original languageEnglish
Number of pages8
Publication statusPublished - 10 Dec 2020
EventMachine Learning for Engineering Modeling, Simulation, and Design
Workshop at Neural Information Processing Systems 2020
-
Duration: 10 Dec 202010 Dec 2020
https://ml4eng.github.io/

Workshop

WorkshopMachine Learning for Engineering Modeling, Simulation, and Design
Workshop at Neural Information Processing Systems 2020
Period10/12/2010/12/20
Internet address

Bibliographical note

8 pages, 4 figures, accepted for Machine Learning for Engineering Modeling, Simulation, and Design Workshop at Neural Information Processing Systems 2020

Keywords

  • stat.CO

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