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ANC Seminar: Umberto Simola (University of Helsinki)

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What
  • ANC/DTC Seminar
When Jun 21, 2018
from 11:00 AM to 12:00 PM
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Title:

Approximate Bayesian Computation for Finite Mixture Models

 

Abstract:

Finite mixture models are used in statistics and other disciplines, but inference for mixture models is challenging. The multimodality of the likelihood function and the so called label switching problem contribute to the challenge. We propose extensions of the Approximate Bayesian Computation Population Monte-Carlo (ABC-PMC) algorithm as an alternative framework for inference on finite mixture models. There are several decisions to make when implementing an ABC-PMC algorithm for finite mixture models, including the selection of the kernel used for moving the particles through the iterations, how to address the label switching problem and the choice of informative summary statistics. Examples are presented to demonstrate the performance of the proposed ABC-PMC algorithm for mixture modeling.

 

Bio:

Umberto Simola is a PhD student currently working in the Department of Mathematics and Statistics at the University of Helsinki. From November 2014 to October 2017 he was a PhD candidate in the Department of Statistical Sciences at the University of Padova, Italy, where he produced the PhD thesis titled: "Developments in Approximate Bayesian Computation and Statistical Applications in Astrostatistics". His research interests are focused on improving the efficiency of the Approximate Bayesian Computation framework and to detecting "Earth-like" Extrasolar planets.