@inproceedings{20a1618a297849a4b4525235fd3a9a24,

title = "The Prior-Predictive Value: A Paradigm of Nasty Multi-Dimensional Integrals",

abstract = "At the heart of Bayesian model comparison lies the so-called prior-predictive value. In the important class of Quantified-MaxEnt applications analytic approximations are routinely used which often give rise to noise-fitting and ringing. We present an improved analytic expression which overcomes these shortcomings. In most interesting real-world problems, however, standard approximations and straight forward application of Markov-Chain Monte Carlo are hampered by the complicated structure of the likelihood in parameter space. At the Maxent workshop 1997 in Boise John Skilling suggested to employ a formalism, borrowed from Statistical physics, to compute the prior-predictive value. We have scrutinized his suggestion: IT WORKS!",

keywords = "Artificial Intelligence (incl. Robotics), Bayes factor, Coding and Information Theory, Discrete Mathematics in Computer Science, MCMC, model comparison, Prior-predictive value, Probability Theory and Stochastic Processes, Statistics, general",

author = "Linden, {W. Von Der} and R. Preuss and V. Dose",

note = "DOI: 10.1007/978-94-011-4710-131",

year = "1999",

language = "English",

isbn = "978-94-010-5982-4 978-94-011-4710-1",

series = "Fundamental Theories of Physics",

publisher = "Springer Netherlands",

pages = "319--326",

editor = "Linden, {Wolfgang von der} and Volker Dose and Rainer Fischer and Roland Preuss",

booktitle = "Maximum Entropy and Bayesian Methods Garching, Germany 1998",

address = "Netherlands",

}