Summary
Statistical inference is the basic toolkit used throughout the whole book. This chapter is intended to offer a short, rather informal introduction to this topic and to compare its two principled paradigms: the frequentist and the Bayesian approach. Mathematical rigour is abandoned in favour of a verbal, more illustrative exposition of this subject, and throughout this chapter the focus will be on concepts rather than details, omitting all proofs and regularity conditions. The main target audience is students and researchers in biology and computer science, who aim to obtain a basic understanding of statistical inference without having to digest rigorous mathematical theory.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
G. Deco and D. Obradovic. An Information-Theoretic Approach to Neural Computing. Springer Verlag, New York, 1996.
R. Durbin, S. R. Eddy, A. Krogh, and G. Mitchison. Biological sequence analysis. Probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge, UK, 1998.
B. Efron. Bootstrap methods: Another look at the jacknife. Annals of Statistics, 7:1–26, 1979.
B. Efron and G. Gong. A leisurely look at the bootstrap, the jacknife, and cross-validation. The American Statistician, 37(1):36–47, 1983.
P. G. Hoel. Introduction to Mathematical Statistics. John Wiley and Sons, Singapore, 1984.
P. J. Krause. Learning probabilistic networks. Knowledge Engineering Review, 13:321–351, 1998.
D. J. C. MacKay. Bayesian interpolation. Neural Computation, 4:415–447, 1992.
R. M. Neal. Bayesian Learning for Neural Networks, volume 118 of Lecture Notes in Statistics. Springer, New York, 1996. ISBN 0-387-94724-8.
A. Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill, Singapore, 3rd edition, 1991.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag London Limited
About this chapter
Cite this chapter
Husmeier, D. (2005). A Leisurely Look at Statistical Inference. In: Husmeier, D., Dybowski, R., Roberts, S. (eds) Probabilistic Modeling in Bioinformatics and Medical Informatics. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-119-9_1
Download citation
DOI: https://doi.org/10.1007/1-84628-119-9_1
Publisher Name: Springer, London
Print ISBN: 978-1-85233-778-0
Online ISBN: 978-1-84628-119-8
eBook Packages: Computer ScienceComputer Science (R0)