Information Criteria and Marginal Likelihood

Joe Suzuki
Additional contact information
Joe Suzuki: Osaka University, Graduate School of Engineering Sciences

Chapter Chapter 7 in Graphical Models and Causal Discovery with Python, 2026, pp 127-149 from Springer

Abstract: Abstract In this chapter, we discuss information criteria (AIC and BIC) and Bayesian marginal likelihood, which play central roles in structure learning for graphical models. We first clarify the definitions and objectives of AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) and explain their positioning as selection methods based on the trade-off between goodness of fit and model complexity. Using linear regression models and categorical data as examples, we show how to compute these criteria concretely.

Date: 2026
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:978-981-95-5308-2_7

Ordering information: This item can be ordered from
http://www.springer.com/9789819553082

DOI: 10.1007/978-981-95-5308-2_7

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 ().