Transcription of Understanding predictive information criteria for Bayesian ...
{{id}} {{{paragraph}}}
Example: air traffic controller
Understanding predictive information criteria for Bayesian ...
Understanding predictive information criteria for Bayesian models . Andrew Gelman , Jessica Hwang , and Aki Vehtari . 14 Aug 2013. Abstract We review the Akaike, deviance, and Watanabe-Akaike information criteria from a Bayesian perspective, where the goal is to estimate expected out-of-sample-prediction error using a bias- corrected adjustment of within-sample error. We focus on the choices involved in setting up these measures, and we compare them in three simple examples, one theoretical and two applied. The contribution of this review is to put all these information criteria into a Bayesian predictive context and to better understand, through small examples, how these methods can apply in practice.
Understanding predictive information criteria for Bayesian models ... are proper and local: propriety of the scoring rule motivates the decision maker to report his or her beliefs honestly, and for local scoring rules predictions are judged only on the plausibility they 2.
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: