9,179 research outputs found
An empirical Bayes approach to identification of modules in dynamic networks
We present a new method of identifying a specific module in a dynamic
network, possibly with feedback loops. Assuming known topology, we express the
dynamics by an acyclic network composed of two blocks where the first block
accounts for the relation between the known reference signals and the input to
the target module, while the second block contains the target module. Using an
empirical Bayes approach, we model the first block as a Gaussian vector with
covariance matrix (kernel) given by the recently introduced stable spline
kernel. The parameters of the target module are estimated by solving a marginal
likelihood problem with a novel iterative scheme based on the
Expectation-Maximization algorithm. Additionally, we extend the method to
include additional measurements downstream of the target module. Using Markov
Chain Monte Carlo techniques, it is shown that the same iterative scheme can
solve also this formulation. Numerical experiments illustrate the effectiveness
of the proposed methods
Probabilistic Methodology and Techniques for Artefact Conception and Development
The purpose of this paper is to make a state of the art on probabilistic methodology and techniques for artefact conception and development. It is the 8th deliverable of the BIBA (Bayesian Inspired Brain and Artefacts) project. We first present the incompletness problem as the central difficulty that both living creatures and artefacts have to face: how can they perceive, infer, decide and act efficiently with incomplete and uncertain knowledge?. We then introduce a generic probabilistic formalism called Bayesian Programming. This formalism is then used to review the main probabilistic methodology
and techniques. This review is organized in 3 parts: first the probabilistic models from Bayesian networks to Kalman filters and from sensor fusion to CAD systems, second the inference techniques and finally the learning and model acquisition and comparison methodologies. We conclude with the perspectives of the BIBA project as they rise from this state of the art
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