Bayesian Post-Model-Selection Estimation

Nadav Harel, Tirza Routtenberg

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Estimation after model selection refers to the problem where the exact observation model is unknown and is assumed to belong to a set of candidate models. Thus, a data-based model-selection stage is performed prior to the parameter estimation stage, which affects the performance of the subsequent estimation. In this letter, we investigate post-model-selection Bayesian parameter estimation of a random vector with an unknown deterministic support set, where this support set represents the model. First, we present different estimators, including the oracle minimum mean-squared-error (MMSE), the coherent MMSE, the selected MMSE, and the full model MMSE. Then, we develop the selective Bayesian Cram$\acute{\text{e}}$r-Rao bound (BCRB) and selective tighter BCRB, which are lower bounds on the mean-squared-error (MSE) for any coherent estimator.

Original languageEnglish
Article number9314021
Pages (from-to)175-179
Number of pages5
JournalIEEE Signal Processing Letters
StatePublished - 1 Jan 2021


  • Bayesian parameter estimation
  • coherence estimation
  • estimation after model selection
  • selective Bayesian Cramer-Rao bound
  • sparse recovery

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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