Practical and theoretical aspects of mixture‐of‐experts modeling: An overview

Abstract

Mixture‐of‐experts (MoE) models are a powerful paradigm for modeling data arising from complex data generating processes (DGPs). In this article, we demonstrate how different MoE models can be constructed to approximate the underlying DGPs of arbitrary types of data. Due to the probabilistic nature of MoE models, we propose the maximum quasi‐likelihood (MQL) approach as a method for estimating MoE model parameters from data, and we provide conditions under which MQL estimators are consistent and asymptotically normal. The blockwise minorization–maximization (blockwise‐MM) algorithm framework is proposed as an all‐purpose method for constructing algorithms for obtaining MQL estimators. An example derivation of a blockwise‐MM algorithm is provided. We then present a method for constructing information criteria for estimating the number of components in MoE models and provide justification for the classic Bayesian information criterion (BIC). We explain how MoE models can be used to conduct classification, clustering, and regression and illustrate these applications via two worked examples.

This article is categorized under:

• Algorithmic Development > Statistics
• Technologies > Structure Discovery and Clustering

Abstract

Schematic diagram of the neural network architecture of a mixture of experts model.