Clustering and Latent Factor Models

Nick Heard ()
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Nick Heard: Imperial College London

Chapter 11 in An Introduction to Bayesian Inference, Methods and Computation, 2021, pp 121-136 from Springer

Abstract: Abstract Hierarchical modelsHierarchical model were previously discussed in Sect. 3.3 . This chapter gives further details of practical Bayesian modelling with hierarchies. In some application contexts, the hierarchies are understood to be known during the data collection process. For example, in the student-grade model of Sect. 6.1 , the hierarchical structure recognised that each row of the data matrix X corresponded to test grades from the same student. In other contexts, the hierarchies may be a subjective construct with associated uncertainty. These hierarchies are characterised by additional unknown parameters, sometimes formulated as discrete clusters and otherwise as continuous latent factors. This chapter considers some more advanced modelling techniques commonly applied in such cases.

Date: 2021
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DOI: 10.1007/978-3-030-82808-0_11

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