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- Mixture model - Wikipedia
In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs
- Bayesian Modelling and Inference on Mixtures of Distributions
This chapter aims to introduce the reader to the construction, prior mod-elling, estimation and evaluation of mixture distributions in a Bayesian paradigm We will show that mixture distributions provide a flexible, para-metric framework for statistical modelling and analysis
- Identifying Bayesian Mixture Models
In this case study I will first introduce how mixture models are implemented in Bayesian inference I will then discuss the non-identifiability inherent to that construction as well as how the non-identifiability can be tempered with principled prior information
- Bayesian Modelling and Inference on Mixtures of Distributions
This chapter aims to introduce the prior modeling, estimation, and evaluation of mixture distributions in a Bayesian paradigm The chapter shows that mixture distributions provide a flexible, parametric framework for statistical modeling and analysis
- Variational Bayesian Model Selection for Mixture Distributions
Mixture models are widely used as computationally con-venient representations for modeling complex probability distributions, and are based on a linear combination of some number of simpler, component distributions
- Chapter 13 Mixture models | Bayesian inference with INLA
Essentially, a mixture model is a convex combination of several statistical distributions that represent the different underlying populations For a recent review on the topic see, for example, Frühwirth-Schnatter, Celeux, and Robert (2018)
- Mixture models with Bayesian networks | Bayes Server
Discover how to build a mixture model using Bayesian networks, and then how they can be extended to build more complex models
- Variational Bayesian Model Selection for Mixture Distributions
Mixture models are widely used as computationally con-venient representations for modeling complex probabili-ty distributions, and are based on a linear combination of some number of simpler, component distributions
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