Machine Learning for Simulation meeting
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Europe/Zurich
Description
Lecture on Transport methods for simulation-based inference by Prof.Youssef Marzouk, Massachusetts Institute of Technology (MIT).
Abstract
Many practical Bayesian inference problems fall into the "likelihood-free" setting, where evaluations of the likelihood function or prior density are unavailable or intractable; instead one can only simulate (i.e., draw samples from) the associated distributions. I will discuss how transportation of measure can help solve such problems, by constructing maps that push prior samples, or samples from a joint parameter-data prior, to the desired conditional distribution. These methods have broad utility for inference in stochastic and generative models, as well as for sequential data assimilation problems. Key issues in this construction center on: (1) the estimation of transport maps from few samples; and (2) parameterizations of monotone maps. I will discuss developments on both fronts, including some recent efforts in joint dimension reduction for conditional sampling in high dimensions.
This is joint work with Ricardo Baptista, Olivier Zahm, and Jakob Zech.
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Meeting ID: 662 2873 2578
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Machine Learning for Simulation meeting
- Zoom Meeting ID
- 66228732578
- Host
- Dalila Salamani
- Alternative hosts
- Graeme A Stewart, Witold Pokorski
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