2016Wiley StatsRef: Statistics Reference OnlineRequires access

Model Selection: B ayesian Information Criterion

Joseph E. Cavanaugh

Open publisher page 16 citations

Abstract

Abstract This article presents an overview of the Bayesian information criterion (BIC), along with its motivation and some of its asymptotic optimality properties. It also compares and contrasts the criterion to the Akaike information criterion (AIC).

About this research paper

What this paper is about

Abstract This article presents an overview of the Bayesian information criterion (BIC), along with its motivation and some of its asymptotic optimality properties. It also compares and contrasts the criterion to the Akaike information criterion (AIC).

Why it matters

OpenAlex reports 16 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Abstract This article presents an overview of the Bayesian information criterion (BIC), along with its motivation and some of its asymptotic optimality properties. It also compares and contrasts the criterion to the Akaike information criterion (AIC).

Key concepts: Akaike information criterion, Bayesian information criterion, Selection (genetic algorithm), Information Criteria, Deviance information criterion, Model selection, Mathematics, Bayesian probability

Related papers

Back to paper searchBrowse research topicsOriginal source
Model Selection: B ayesian Information Criterion — Research Paper | ScholarLens