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Activity Number: 107 - The ABC of Making an Impact
Type: Invited
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #300329 Presentation
Title: Simulated Annealing ABC (SABC) and Its Application to a Stochastic Solar Dynamo Model
Author(s): Carlo Albert*
Companies: Swiss Federal Institute of Aquatic Science and Technology (Eawag)
Keywords: Approximate Bayes Computation; Simulated Annealing; Machine Learning; Solar Physics

When lots of data is available, but there's little prior knowledge about model parameters, convergence of ABC algorithms is typically enhanced through gradually decreasing the tolerance between simulated and measured data, akin to simulated annealing (SA) in optimization. SA-ABC (short SABC) employs an adaptive annealing schedule that attempts to minimize entropy production, which is a measure for wasted computation, within the particle ensemble. SABC has shown superior performance in many applications. It requires little tuning and does not suffer from an N^2 overhead, unlike other ABC algorithms that gradually decrease the tolerance, which is a big advantage on parallel computing infrastructures. After an introduction to SABC, I will show new inference results, for stochastic solar dynamo models calibrated to radionuclide time series. I will also report on our experience with machine learning techniques, for the generation of summary statistics from time-series.

Authors who are presenting talks have a * after their name.

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