The Bayesian information criterion: background, derivation, and applications

By Andrew A. Neath, Joseph E. Cavanaugh. 2012

953 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/45c625597fb70f457f63ed4c96dca90f24a83e23

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. B. Jiang
2. William C. Sullivan
3. Chun-Yen Chang

In Statistics and Probability

1. Raúl Tempone
2. Ivo Babuska
3. B. Jiang
4. Barna Szabó
5. William C. Sullivan
6. Marco Scavino
7. Chun-Yen Chang
8. Zaid Sawlan

Citations in the corpus

• A dose of nature: Tree cover, stress reduction, and gender differences (2014, methodology): “For data with small or moderate sample sizes, BIC is superior to other popular model selection criteria (Neath & Cavanaugh, 2012).”
• Bayesian inference and model comparison for metallic fatigue data (2015, methodology): “43) (-3.11, 3.36) 3.3. Model comparison Using a classical approach, we compute some popular information criteria, such as Akaike information criterion (AIC) [18], Bayesian information criterion (BIC) [19, 20] and AIC with correction [21], which are based on the maximized log-likelihood values. Such ”