Abstract
Consider a statistical model, given by the distribution of the observation X, conditional on the parameter θ, and the prior distribution of the parameter θ. Let H x denote the function that maps the prior mean and the prior covariance matrix into the posterior mean and the posterior covariance matrix, when X = x is observed. We prove that if the conditional distribution of X belongs to an exponential family, then the function H x characterizes the distribution of X∥ θ.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
