13.1 finding post modes

conditional max
newton’s method
quasi-newton and conjugate gradient methods
numerical computation of derivatives

13.2 boundary-avoiding priors for modal summaries

posterior modes on the boundary of param space

zero-avoiding prior dist for a group level variance param

13.3 normal and related mixture approx

13.4 finding marginal post modes using em
derivation of the em and generalized em
implementation of em

13.5 approx cond and marginal post dens

13.6 hierarchical norml model

13.7 variational inference
mini of kld
class of approx dist
variational bayes alg
-educational testing exp
determining the conditional exp
proof that each step decrease kld
model checking
variational bayes followed by importance sampling or particle filtering
em as a special case of bayes
more general forms of variational bayes

13.8 expectation propagation
expectation propagation for logistic regression
– bioassay logistic regression with two coefficients

13.9 other approx
integrated nested laplace approx
approx bayesian computation

13.10 unknown normalizing factors