On Adaptive Bayesian Inference

Research output: Contribution to journalArticle


We study the rate of Bayesian consistency for hierarchical priors consisting of prior weights on a model index set and a prior on a density model for each choice of model index. Ghosal, Lember and Van der Vaart [2] have obtained general in-probability theorems on the rate of convergence of the resulting posterior distributions. We extend their results to almost sure assertions. As an application we study log spline densities with a finite number of models and obtain that the Bayes procedure achieves the optimal minimax rate $n^{-\gamma/(2\gamma+1)}$ of convergence if the true density of the observations belongs to the H\"{o}lder space $C^{\gamma}[0,1]$. This strengthens a result in [1; 2]. We also study consistency of posterior distributions of the model index and give conditions ensuring that the posterior distributions concentrate their masses near the index of the best model.


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  • External Organization - Unknown
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Mathematics


  • log spline density., density function, posterior distribution, rate of convergence, Adaptation
Original languageEnglish
Pages (from-to)848-863
JournalElectronic Journal of Statistics
Publication statusPublished - 2008
Publication categoryResearch
Externally publishedYes