Objective Bayesian Analysis of Multiple Changepoints for Linear Models
نویسندگان
چکیده
This paper deals with the detection of multiple changepoints for independent but non identically distributed observations, which are assumed to be modeled by a linear regression with normal errors. The problem has a natural formulation as a model selection problem and the main difficulty for computing model posterior probabilities is that neither the reference priors nor any form of empirical Bayes factors based on real training samples can be employed.
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