Choice of Basis for Laplace Approximation
作者：David J.C. MacKay
摘要
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.
论文关键词：Bayesian inference, graphical models, Bayes factor, marginal likelihood, hidden Markov models, latent variable models
论文评审过程：
论文官网地址：https://doi.org/10.1023/A:1007558615313
