Using the Bayesian Shtarkov solution for predictions

Tri Le, Bertrand Clarke

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)183-196
Number of pages14
JournalComputational Statistics and Data Analysis
StatePublished - Dec 1 2016


  • Bagging
  • Bayes
  • Model average
  • Prequential
  • Shtarkov predictor
  • Stacking

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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