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Using the Bayesian Shtarkov solution for predictions

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  • Le, Tri
  • Clarke, Bertrand

Abstract

The Bayes Shtarkov predictor can be defined and used for a variety of data sets that are exceedingly hard if not impossible to model in any detailed fashion. Indeed, this is the setting in which the derivation of the Shtarkov solution is most compelling. The computations show that anytime the numerical approximation to the Shtarkov solution is ‘reasonable’, it is better in terms of predictive error than a variety of other general predictive procedures. These include two forms of additive model as well as bagging or stacking with support vector machines, Nadaraya–Watson estimators, or draws from a Gaussian Process Prior.

Suggested Citation

  • Le, Tri & Clarke, Bertrand, 2016. "Using the Bayesian Shtarkov solution for predictions," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 183-196.
    • Handle: RePEc:eee:csdana:v:104:y:2016:i:c:p:183-196
    DOI: 10.1016/j.csda.2016.06.018
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    References listed on IDEAS

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    1. Clarke, Bertrand, 2007. "Information optimality and Bayesian modelling," Journal of Econometrics, Elsevier, vol. 138(2), pages 405-429, June.
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