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Binning-Independent Bayesian Analysis of Time-Dependent Perturbed Angular Distribution data
We propose a new approach for analyzing Time-Dependent Perturbed Angular Distribution data. In this, a likelihood is constructed with the help of conditional probabilities of single events and binning of time data is avoided. This likelihood is used in a Bayesian framework to calculate the posterior
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