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A note on maximized likelihood sets
Affiliation:1. Mechanical and Industrial Engineering, Northeastern University, 360 Huntington Avenue, 334 SN, Boston, MA 02115, USA;2. School of Mathematical and Statistical Sciences, Clemson University, O-110 Martin Hall, Clemson, SC 29631, USA;1. Department of Stochastic Simulation and Safety Research for Hydrosystems (IWS/SC SimTech), University of Stuttgart, Germany;2. Federal Waterways Engineering and Research Institute (BAW), Karlsruhe, Germany
Abstract:The purpose of this note is to show that the posterior measure under a partial prior information, which is constructed on the maximized likelihood function,is compatible with the Bayesian properties of the likelihood sets.
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