This paper describes a general model that subsumes many parametric models for continuous data.The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another.The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity.Special cases range from the general linear model for static omnipure k2533 data to generalised convolution models, with system noise, for nonlinear time-series analysis.
Crucially, all of these models can be inverted using exactly the same scheme, namely, dynamic expectation maximization.This means that a single model and optimisation scheme can be used to invert a wide range of models.We present the model and a brief review of its inversion to disclose the relationships among, apparently, diverse generative models of empirical data.We then show that this inversion can be sukin body lotion woolworths formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain.