Quotation Frühwirth-Schnatter, Sylvia, Zens, Gregor. 2022. Sparse Mixture-Of-Experts Models. Austrian and Slovenian Statistical Days 2022, Graz, Austria, 20.04.-22.04.


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Abstract

Selecting the appropriate number of clusters is a long-standing problem in mixture modeling and model-based clustering in general. A number of successful approaches propose to treat the number of clusters as a random quantity to be estimated. However, such methodology is not available for mixture-of-experts models, a model class where covariates are used to inform cluster membership. We aim to fill this gap and develop a flexible Bayesian mixture framework that combines covariate-dependent mixture weights and endogenous estimation of the number of mixture clusters. We show that model selection procedures based on overfitting finite mixtures can be extended to the class of mixture-of-experts models and derive the implied prior distributions. Finally, we outline an accurate Markov chain Monte Carlo sampling scheme for efficient posterior simulation. The utility of the framework is illustrated using simulated data and a real world example.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title Sparse Mixture-Of-Experts Models
Event Austrian and Slovenian Statistical Days 2022
Year 2022
Date 20.04.-22.04.
Country Austria
Location Graz

Associations

Projects
Shrinking and Regularizing Finite Mixture Models
People
Frühwirth-Schnatter, Sylvia (Details)
Zens, Gregor (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1105 Computer software (Details)
1113 Mathematical statistics (Details)
5701 Applied statistics (Details)
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