mixture of Gaussians models
(50 minutes to learn)
Mixture of Gaussians is a probabilistic model commonly used for clustering: partitioning a set of data points into a set of clusters, where data points within a cluster are similar to one another.
This concept has the prerequisites:
Core resources (read/watch one of the following)
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location: Section 9.2, up to 9.2.1, pages 430-432
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location: Section 11.2, pages 337-342
Supplemental resources (the following are optional, but you may find them useful)
→ Bayesian Reasoning and Machine Learning
A textbook for a graudate machine learning course.
- Bayesian networks
- K-means is a simpler clustering model which is faster to fit and often used as an initialization.
- When there isn't enough data to fit a general mixture of Gaussians, here are some alternative models:
- Bayesian mixture of Gaussians
- mixture of factor analyzers
- Bayesian clustered tensor factorization
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