Various proposals have recently been made which cast cortical processing in terms of hierarchical statistical generative models (Mumford, 1994; Kawato, 1993; Hinton & Zemel, 1994; Zemel, 1994; Hinton et al , 1995; Dayan et al , 1995; Olshausen & Field, 1996; Rao & Ballard, 1995). In the case of vision, these claim that top-down connections in the cortical hierarchy capture essential aspects of ...

This paper presents a new technique for modelling object classes (such as faces) and matching the model to novel images from the object class. The technique can be used for a variety of image analysis applications including face recognition, object veri cation and facial expression analysis. The model, called a hierarchical morphable model, is \learned" from example images (partioned into compo...

There is general agreement that we need to understand provenance at various levels of granularity; however, there appears, as yet, to be no general agreement on what granularity means. It can refer both to the detail with which we can view a process or the detail with which we view the data. We describe a simple and straightforward method for imposing a hierarchical structure on a provenance gr...

The reflexion model originally proposed by Murphy and Notkin allows one to structurally validate a descriptive or prescriptive architecture model against a source model. First, the entities in the source model are mapped onto the architectural model, then discrepancies between the architecture model and source model are computed automatically. The original reflexion model allows an analyst to s...

Relationship to empirical Bayes and RL. The augmentation with a variational prior has strong ties to empirical Bayesian methods, which use data to estimate hyperparameters of a prior distribution (Robbins, 1964; Efron & Morris, 1973). In general, empirical Bayes considers the fully Bayesian treatment of a hyperprior on the original prior—here, the variational prior on the original meanfield—and...

Bayesian models involving Dirichlet process mixtures are at the heart of the modern nonparametric Bayesian movement. Much of the rapid development of these models in the last decade has been a direct result of advances in simulation-based computational methods. Some of the very early work in this area, circa 1988-1991, focused on the use of such nonparametric ideas and models in applications of...

Multinomial processing tree models are widely used in many areas of psychology. Their application relies on the assumption of parameter homogeneity, that is, on the assumption that participants do not differ in their parameter values. Tests for parameter homogeneity are proposed that can be routinely used as part of multinomial model analyses to defend the assumption. If parameter homogeneity i...