Indirect density estimation using the iterative Bayes algorithm

Jun Ma*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)


    Many practical problems involve density estimation from indirect observations and they are classified as indirect density estimation problems. For example, image deblurring and image reconstruction in emission tomography belong to this class. In this paper we propose an iterative approach to solve these problems. This approach has been successfully applied to emission tomography (Ma, 2008). The popular EM algorithm can also be used for indirect density estimation, but it requires that observations follow Poisson distributions. Our method does not involve such assumptions; rather, it is established simply from the Bayes conditional probability model and is termed the Iterative Bayes (IB) algorithm. Under certain regularity conditions, this algorithm converges to the positively constrained solution minimizing the KullbackLeibler distance, an asymmetric measure involving both logarithmic and linear scales of dissimilarities between two probability distributions.

    Original languageEnglish
    Pages (from-to)1180-1195
    Number of pages16
    JournalComputational Statistics and Data Analysis
    Issue number3
    Publication statusPublished - 1 Mar 2011


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