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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Machine learning 33 (1998), S. 77-86 
    ISSN: 0885-6125
    Keywords: Bayesian inference ; graphical models ; Bayes factor ; marginal likelihood ; hidden Markov models ; latent variable models
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Maximum a posteriori optimization of parameters and the Laplace approximation for the marginal likelihood are both basis-dependent methods. This note compares two choices of basis for models parameterized by probabilities, showing that it is possible to improve on the traditional choice, the probability simplex, by transforming to the 'softmax' basis.
    Type of Medium: Electronic Resource
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