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Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
Type: Invited
Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #327036
Title: Approximate Bayesian Computation for the Stellar Initial Mass Function
Author(s): Jessi Cisewski-Kehe* and Chad Schafer and Grant Weller and David Hogg
Companies: Yale University and Carnegie Mellon University and Savvysherpa and New York University
Keywords: astrostatistics; approximate bayesian computation; computational statistics; inference; physical sciences

Explicitly specifying a likelihood function is becoming increasingly difficult for many problems in astronomy. Astronomers often specify a simpler approximate likelihood - leaving out important aspects of a more realistic model. Estimation of a stellar initial mass function (IMF) is one such example. The stellar IMF is the mass distribution of stars initially formed in a particular volume of space, but is typically not directly observable due to stellar evolution and other disruptions of a cluster. Several difficulties associated with specifying a realistic likelihood function for the stellar IMF will be included.

Approximate Bayesian computation (ABC) provides a framework for performing inference in cases where the likelihood is not available. I will introduce ABC, and demonstrate its merit through a simplified IMF model where a likelihood function is specified and exact posteriors are available. To aid in capturing the dependence structure of the data, a new formation model for stellar clusters using a preferential attachment framework will be presented. The proposed formation model, along with ABC, provides a new mode of analysis of the IMF.

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

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