  A new method is introduced which uses higher-order Laplace approximation to
evaluate functional integrals much faster than existing methods. An
implementation in MATLAB is called SLAM-FIT (Sparse Laplace Approximation
Method for Functional Integration on Time) or simply SLAM. In this paper SLAM
is applied to estimate parameters of mixed models that require functional
integration. It is compared with two more general packages which can be used to
do functional integration. One is Stan, a recent and very general package for
integrating and estimating using hybrid Monte Carlo. The other is INLA, a
recent R package which uses Laplace approximations for Gaussian Markov random
fields. In both cases it is able to get near-identical or equivalent results,
in significantly less time, even for moderately sized data sets. The
fundamental speed advantage of the algorithm may be greater than it appears,
because SLAM is running in pure MATLAB while the other two packages use
optimized compiled code.
