Novel approximations for inference and learning in nonlinear dynamical systems
Alexander Ypma, Tom Heskes · Radboud Repository (Radboud University) · 2004
A b stract.We formulate the problem of inference in nonlinear dynami cal systems in the Expectation-Propagation framework, and propose two novel inference algorithms based on Laplace approximation and the Un scented transform.The algorithms are compared empirically and em ployed as an improved E-step in a conjugate gradient learning algorithm.We illustrate its use for data mining with two high-dimensional time series from marketing research.