Graphical Modelling and Hierarchical Models

Nick Heard ()
Additional contact information
Nick Heard: Imperial College London

Chapter 3 in An Introduction to Bayesian Inference, Methods and Computation, 2021, pp 23-32 from Springer

Abstract: Abstract In many contexts, straightforward exchangeability can be a useful simplifying assumption for specifying the joint probability distribution of random variables. But sometimes an individual will require more complex structures of statistical dependence between random quantities to properly represent their beliefs. Graphical models provide a useful framework for characterising joint distributions for random variables, putting primary focus on characterising uncertainty in the dependency structure amongst the variables. Much of the material in this chapter is drawn from Barber (2012) and related resources. Before introducing graphical models, some basic graph concepts and definitions are required to provide a language for relating probability distributions to graphs.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-82808-0_3

Ordering information: This item can be ordered from
http://www.springer.com/9783030828080

DOI: 10.1007/978-3-030-82808-0_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().