Abstract
The Bayesian interpretation of probabilities has a natural appeal in the context of pattern analysis and forecasting in the way that it provides an intuitive and flexible framework for dealing with many different possible hypotheses, even in situations when data is scarce. As a consequence, Bayesian methods have enjoyed a much wider following in this subject area than they perhaps have in the wider statistical community. Since Bayesian thinking is often implied in many of the methods described throughout this book, we feel that it is worthwhile outlining briefly what we mean by Bayesian methods and the type of statistical analysis to which it naturally lends itself.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag London
About this chapter
Cite this chapter
Larsson, S. (2002). Bayesian Methods and Evidence. In: Shadbolt, J., Taylor, J.G. (eds) Neural Networks and the Financial Markets. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0151-2_15
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0151-2_15
Publisher Name: Springer, London
Print ISBN: 978-1-85233-531-1
Online ISBN: 978-1-4471-0151-2
eBook Packages: Springer Book Archive