  A software library for constructing and learning probabilistic models is
presented. The library offers a set of building blocks from which a large
variety of static and dynamic models can be built. These include hierarchical
models for variances of other variables and many nonlinear models. The
underlying variational Bayesian machinery, providing for fast and robust
estimation but being mathematically rather involved, is almost completely
hidden from the user thus making it very easy to use the library. The building
blocks include Gaussian, rectified Gaussian and mixture-of-Gaussians variables
and computational nodes which can be combined rather freely.
