Silvia Chiappa

Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition

We discuss a method to extract independent dynamical systems underlying a single or multiple channels of observation. In particular, we search for one-dimensional subsignals to aid the interpretability of the decomposition. The method uses an approximate Bayesian analysis to determine automatically the number and appropriate complexity of the underlying dynamics, with a preference for the simplest solution. We apply this method to unfiltered EEG signals to discover low-complexity sources with preferential spectral properties, demonstrating improved interpretability of the extracted sources over related methods

Bayesian Time Series Models: Inference and estimation in probabilistic time series models

© Cambridge University Press 2011. The term ‘time series’ refers to data that can be represented as a sequence. This includes for example financial data in which the sequence index indicates time, and genetic data (e.g. ACATGC …) in which the sequence index has no temporal meaning. In this tutorial we give an overview of discrete-time probabilistic models, which are the subject of most chapters in this book, with continuous-time models being discussed separately in Chapters 4, 6, 11 and 17. Throughout our focus is on the basic algorithmic issues underlying time series, rather than on surveying the wide field of applications.