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SIAM Conference on Uncertainty Quantification (UQ20)

Friday – 27.03.2020

08:30

iCal
Christian P. Robert | Universite Paris-Dauphine | France

Florence Forbes | Universite Grenoble Alpes, Inria, CNRS | France

Matti Vihola | University of Jyvaskyla | Finland

Ann Lee | Carnegie Mellon University | United States

MS751: Advances in likelihood-free inference (Part I of II)

Chair(s)
Ricardo Baptista (Massachusetts Institute of Technology)

Giulio Trigila (Baruch College of New York)

Giulio Trigila (Baruch College of New York)

Room:
IAS 4.001

Topic:
Inverse problems

Form of presentation:
Mini-symposium

Duration:
120 Minutes

Many statistical models of interest in engineering, the sciences, and machine learning define a likelihood function that is computationally prohibitive to evaluate. This may be induced from the model only being known through a data generating process or the likelihood function involving a high-dimensional integral (e.g., from a marginalization procedure or the computation of a normalizing constant). In these cases, it is difficult to apply classical inference methods such as maximum likelihood estimation or likelihood-based Bayesian inference algorithms. To enable inference in these settings, several approaches have been developed in the statistics and machine learning community that avoid direct evaluation of the likelihood function (e.g., approximate Bayesian computation). Despite these success, efficiently solving such problems remains challenging, especially in high dimensions, or when only limited information or few samples are available. This mini-symposium will explore new algorithms and methodologies for performing likelihood-free inference in these complex models.

08:30

Component-wise approximate Bayesian computation via Gibbs-like steps

09:00

Approximate Bayesian computation via the energy statistic

09:30

On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction

10:00

Confidence Regions and Hypothesis Testing in a Likelihood-Free Inference Setting