… Variational Bayes, under the Laplace approximation, assumes a fixed Gaussian form for the
… summarise the basic theory of variational Bayes and apply it in the context of the Laplace …

… The theoretical section is completed by relating VB to Laplace approximations … Bayesian
model comparison. Numerical examples are then given showing how VB differs from Laplace …

… variational Bayesian inference in conjunction with quadratic or Laplace approximations of
the … of quadratic approximations and Laplace's method, which we will first reprise. A quadratic …

… that complete the portfolio of existing analyses of variational Bayesian approaches, … Laplace
approximation to go from the first to the second line, and the expression for the variational …

… Friston et al. (2007) have also applied their variational Laplace method to non-linear models
by way of a Taylor expansion, this time assuming that the model is weakly non-linear and …

… Variational Bayes also provides a lower-… inference on the states of a linear convolution
model. The only difference is that here, we have used a Laplace approximation to the variational …

… Friston et al. (2007) have also applied their variational Laplace method to non-linear models
by way of a Taylor expansion, this time assuming that the model is weakly non-linear and …

… parameters of any model (DCM) under the Laplace approximation. In other words, it assumes
that … The variational Bayesian EM algorithm for incomplete Data: with application to scoring …

… is itself a special case of variational Bayes. In fact, these … variational approximation to the
approximating posterior under the Laplace approximation and the mean field approximation…

… By adopting the perspective of PC as a variational Bayes algorithm under the Laplace
approximation, we identify the source of these deficits to lie in the exclusion of an associated …