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
URL:
http://dx.doi.org/10.1023/A:1007558615313
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