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In many applications, Bayesian methods are increasingly recognized to yield high-quality results, while providing an accurate measure of uncertainty in the form of posterior distributions. However, they have also obtained a reputation of requiring enormous computational effort and being difficult to use, due to the expertise required in choosing prior distributions. In this article, we focus on Bayesian inference in Hidden Markov Models, which are central to segmentation tasks such as identification of Copy Number Variants (CNV). We achieve dramatic improvements in both speed and accuracy through the use of dynamic wavelet compression and automatically selected priors. Wiedenhoeft et al.
Image Credit: Wiedenhoeft et al.
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Applying, Evaluating and Refining Bioinformatics Core Competencies (An Update from the Curriculum Task Force of ISCB’s Education Committee)
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