We address the problem of Bayesian estimation where the statistical relation between the signal and measurements is only partially known. We propose modeling partial Bayesian knowledge by using an auxiliary random vector called instrument. The statistical relations between the instrument and the signal and between the instrument and the measurements, are known. However, the joint probability function of the signal and measurements is unknown. Two types of statistical relations are considered, corresponding to second-order moment and complete distribution function knowledge. We propose two approaches for estimation in partial knowledge scenarios. The first is based on replacing the orthogonality principle by an oblique counterpart and is proven to coincide with the method of instrumental variables from statistics, although developed in a different context. The second is based on a worst-case design strategy and is shown to be advantageous in many aspects. We provide a thorough analysis showing in which situations each of the methods is preferable and propose a nonparametric method for approximating the estimators from a set of examples. Finally, we demonstrate our approach in the context of enhancement of facial images that have undergone unknown degradation and image zooming.

Original languageEnglish
Article number5711686
Pages (from-to)1933-1948
Number of pages16
JournalIEEE Transactions on Signal Processing
Issue number5
StatePublished - May 2011


  • Bayesian estimation
  • instrumental variables
  • minimax regret
  • nonparametric regression
  • partial knowledge

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


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