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Exact Bayesian Regression of Piecewise Constant Functions
, 2007
"... We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary locations, and levels. The derivation works for any noise and segment level prior, e.g. Cauchy which can handle outliers. We derive simple but good estimates for the in ..."
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Cited by 8 (3 self)
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We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary locations, and levels. The derivation works for any noise and segment level prior, e.g. Cauchy which can handle outliers. We derive simple but good estimates for the insegment variance. We also propose a Bayesian regression curve as a better way of smoothing data without blurring boundaries. The Bayesian approach also allows straightforward determination of the evidence, break probabilities and error estimates, useful for model selection and significance and robustness studies. We discuss the performance on synthetic and realworld examples. Many possible extensions are discussed.
binning: ‘Latency is where the signal starts’
"... Feature extraction from spike trains with Bayesian ..."
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BioMed Central Research article
, 2009
"... This is an Open Access article distributed under the terms of the Creative Commons Attribution License ..."
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License