Abstract

Dynamic models are, in a broad sense, probabilistic models that describe a set of observable measurements conditionally on a set of latent or hidden state-space variables whose time and/or space dynamics are driven by a set of time-invariant parameters. This inherently hierarchical description renders dynamic models to the status of one of the most popular statistical structures in many areas of applied science, including neuroscience, marketing, oceanography, financial markets, target-tracking, signal process, climatology and text analysis, to name just a few. Borrowing the notation from [3], also used by [13], the general structure of a dynamic model can be written as

Measurements model:

[data| state-space, parameters]

State-space dynamics:

[state-space| parameters]

Parameters prior:

[parameters]