Metrics
1 Record Views
Book chapter
Deriving Mixture Distributions Using Moment Generating Functions: A Hierarchical Model Approach
Mathematics and Computer Science: Research Updates Vol. 11, pp.42-55
BP Publishing
2026
Abstract
Mixture distributions are important because they model data from multiple underlying subpopulations, allowing us to capture heterogeneity that a single distribution can’t explain. Generally, mixture distributions arise as marginal distributions of hierarchical mixture models. In this chapter, we use moment-generating functions (mgfs) to derive the densities of mixture distributions from hierarchical models. When the mgf of a mixture distribution doesn’t exist, the approach can be extended to characteristic functions to derive the mixture density. This chapter uses a result from Villa and Escobar (2006). The present work complements Villa and Escobar’s (2006) article with many new examples.
Related links
Details
- Title
- Deriving Mixture Distributions Using Moment Generating Functions: A Hierarchical Model Approach
- Publication Details
- Mathematics and Computer Science: Research Updates Vol. 11, pp.42-55
- Resource Type
- Book chapter
- Publisher
- BP Publishing
- Copyright
- © BP Publishing
- Identifiers
- 99381819760706600
- Academic Unit
- Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- English