Abstract
We present an overview of recent work on a flexible frame-work for multiscale modeling of Poisson count data, such as is encountered regularly in the field of high-energy astrophysics, that allows for intuitive, easily interpretable, computationally efficient implementations of Bayesian inference for standard tasks like smoothing, deconvolution, and segmentation. At the foundation of this approach is a multiscale factorization of the Poisson likelihood, which can be viewed formally as deriving from a blending of concepts from the literatures on wavelets, recursive partitioning, and graphical models.
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Kolaczyk, E.D. (2003). Bayesian Multiscale Methods for Poisson Count Data. In: Statistical Challenges in Astronomy. Springer, New York, NY. https://doi.org/10.1007/0-387-21529-8_6
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DOI: https://doi.org/10.1007/0-387-21529-8_6
Publisher Name: Springer, New York, NY
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