Commission B3 Astroinformatics and Astrostatistics

 

News

  • If you have been wanting to stay up to date on the happenings in the astrostatistics community, or to learn more about astrostatistics, I have good news for you...our first issue of Astrostatistics News came out today!  Astrostatistics News (AN) is a newsletter designed to inform, promote, cultivate, and inspire the astrostatistics community.  AN serves the astrostatistics community by highlighting and describing recent research developments in astrostatistics at an accessible level to the diverse backgrounds of its members, sharing interesting new algorithms, software, or data sets, promoting relevant events, and striving to inspire new researchers to join in the fun.  We anticipate 2 - 3 issues per year, with the potential for more.

    To subscribe to Astrostatistics News, go to https://groups.google.com/g/astrostatistics-news and select the “Join group” button.  You will need to be logged into your Google account to join the group.

 

Christian Robert (Paris Dauphine University, Paris)

Bayesian approaches to inferring the number of components in a mixture

Abstract: Estimating the model evidence - or marginal likelihood of the data - is a notoriously difficult task for finite and infinite mixture models and we reexamine here different Bayesian approaches and Monte Carlo techniques advocated in the recent and not so recent literature, as well as novel approaches based on Geyer (1994) reverse logistic regression technique, Chib (1995) algorithm, and Sequential Monte Carlo (SMC). Applications are numerous. In particular, testing for the number of components in a finite mixture model or against the fit of a finite mixture model for a given dataset has long been and still is an issue of much interest, albeit yet missing a fully satisfactory resolution. Using a Bayes factor to find the right number of components K in a finite mixture model is known to provide a consistent procedure. We furthermore establish the consistence of the Bayes factor when comparing a parametric family of finite mixtures against the nonparametric 'strongly identifiable' Dirichlet Process Mixture (DPM) model.

Back to Commission Homepage

 

Donate to the IAU

Donate to the IAU

General Assembly 2024

IAU General Assembly 2024

IAU Strategic Plan 2020–2030

Strategic Plan

IAU Code of Conduct

Code of Conduct

Symposia and Meetings

Meetings

Membership

How to Become a Member

Deceased Members

Deceased Members

Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference

CPS

IAU Catalyst

Latest Catalyst

IAU e-Newsletter
Volume 2023 n° 2

Latest e-Newsletter

Subscribe to the e-Newsletter

CAPj

IAU Office of Astronomy for Development

Office for Astronomy Development

IAU Office for Young Astronomers

Office for Young Astronomers

IAU Office for Astronomy Outreach

Office for Astronomy Outreach

IAU Office of Astronomy for Education

Office of Astronomy for Education

International School for Young Astronomers

International School for Young Astronomers

WG Small Bodies Nomenclature Bulletins

WG Small Bodies Nomenclature Bulletins

IAU WG Women in Astronomy Newsletters and Ensemble Magazine

WG Women in Astronomy Newsletters