Details of Grant 

EPSRC Reference:	GR/M27197/01
Title:	CRITICAL EVALUATION OF BAYESIAN METHODS IN NEURAL NETWORKS
Principal Investigator:	Niranjan, Professor M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department:	Computer Science
Organisation:	University of Sheffield
Scheme:	Standard Research (Pre-FEC)
Starts:	01 April 1999	Ends:	30 September 2002	Value (£):	210,328
EPSRC Research Topic Classifications:	New & Emerging Comp. Paradigms
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:
Summary on Grant Application Form
The project is aimed at a critical evaluation of some Bayesian Inference methods in the context of neural computing. These ideas include: (a) the Laplace approximations of posterior probabilities (or evidence approximation); (b) Gaussian process interpolation models as mechanisms for noise reduction and signal enhancement; and (c) Bayesian methods in sequential learning environments. Though the mathematical framework of algorithms derived from this perspective is an elegant one, their usefulness in real life problems is not clear as several approximations are usually performed. In this study, I propose to carry out a critical evaluation of Bayesian methods on a range of problems taken from real world examples taken from Signal Processing, Medicine and Finance.
Key Findings
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Potential use in non-academic contexts
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Impacts
Description This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk Summary Date Materialised
Sectors submitted by the Researcher
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Project URL:
Further Information:
Organisation Website:	http://www.shef.ac.uk