Predictive Bayesian Model Selection
By Tomohiro Ando. 2011
123 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/2077ae8e5103491a041e4d2737f5740493dd7cea
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
- Bradley P. Carlin
- David J. Spiegelhalter
- N. G. Best
- M. Fedele
- A. van der Linde
- M. Valli
- Marco Ciuchini
- L. Silvestrini
In Statistics and Probability
- Bradley P. Carlin
- David J. Spiegelhalter
- N. G. Best
- M. Fedele
- A. van der Linde
- M. Valli
- Marco Ciuchini
- L. Silvestrini
Citations in the corpus
- Sensitivity and Specificity of Information Criteria (2018, unknown): “However, it has some relationship to AIC and has an analogous purpose (Claeskens and Hjort, 2008; Ando, 2013).”
- B → K∗ℓ+ℓ− decays at large recoil in the Standard Model: a theoretical reappraisal (2015, methodology): “We close this section by comparing the above scenarios using the Information Criterion [76, 77], defined as IC = −2logL+ 4σ(2) logL , (3.”
- New Physics in $${\varvec{b \rightarrow s \ell ^+ \ell ^-}}$$ confronts new data on lepton universality (2019, unknown): “[214],”
- The deviance information criterion: 12 years on (2014, methodology): “van der Linde (2005, 2012), Plummer (2008) and Ando (2012) have all concluded that DIC overfits because it uses a plug-in estimate in the predictive target rather than the proper predictive distribution, and all have concluded that a better method would be to increase the penalty: DICÅ =D.θ̄/+3pD =…”