References and further readings
Below you can find a list of books and articles that we have found useful
when trying to gain some understanding of the concept of "data-analysis".
In the list there are those writings we directly refer to but also
some general writings that are related to the approach used by B-Course.
Below you can find some essential references, the list is updated
periodically. It is not intended to be an exhaustive list, and eventually
it will also be partly annotated.
Bayesian probability and reasoning in general
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Berger, J.
Statistical Decision Theory and Bayesian Analysis.
Springer-Verlag, New York, 1985.
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Bernardo, J., Smith, A.
Bayesian theory
John Wiley, Chichester, 1994.
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Box, G., Tiao, G.
Bayesian Inference in Statistical Analysis.
John Wiley & Sons, 1973, (Wiley Classics Library Edition 1992).
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Congdon, P. Bayesian Statistical Modelling. John Wiley & Sons,
2001.
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Corfield, D., Williamson, J. (eds.) Foundations of Bayesianism.
Applied Logic Series, Vol. 24. Kluwer Academic Publishers, 2001.
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Gillies, D. Philosophical Theories of Probability. Routledge, 2000.
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Howson, C., Urbach, P.
Scientific reasoning : the Bayesian approach.
Open Court, Chigago, 1993.
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Jeffreys, H.
Theory of Probability.
Clarendon Press,Oxford, 1939.
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Lindley, D.
Making decisions.
John Wiley & Sons, 2nd Edition 1985.
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Matthews, R.
"Faith, hope and statistics".
New Scientist 156, 2109(22 Nov 1997), 36-39.
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Robert, C.
The Bayesian choice. A Decision-Theoretic Motivation.
Springer, New York, 1994.
Bayesian networks
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Cowell, R., Dawid P.A., Lauritzen S., Spiegelhalter D.
Probabilistic Networks and Expert Systems.
Springer, New York, 1999.
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Jensen, F.
An Introduction to Bayesian Networks.
UCL Press, London, 1996.
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P.Myllymäki, T.Silander, H.Tirri, P.Uronen, B-Course: A Web-Based Tool
for Bayesian and Causal Data Analysis. International Journal on
Artificial Intelligence Tools, Vol 11, No. 3 (2002) 369-387.
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Pearl, J.
Probabilistic Reasoning in Intelligent Systems:
Networks of Plausible Inference.
Morgan Kaufmann Publishers, San Mateo, CA, 1988.
Inferred causation
- Glymour, C., Cooper, G. (eds.)
Computation, Causation & Discovery.
AAAI Press/The MIT Press, Menlo Park 1999.
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Pearl, J.
Causality: Models, Reasoning and Inference.
Cambridge Unversity Press, 2000.
Technicalities
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Heckerman, D.
A tutorial on learning with Bayesian networks.
Tech. Rep. MSR-TR-95-06, Microsoft Research, Advanced Technology Division,
One Microsoft Way, Redmond, WA 98052, 1996.
Bayesian classification
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Denison, D.G.T., Holmes, C.C., Mallick, B.K., Smith, A.F.M. Bayesian
Methods for
Nonlinear Classification and Regression.Wiley Series in Probability
and Statistics.
John Wiley & Sons, 2002.
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P.Kontkanen, P.Myllymäki, T.Silander, H.Tirri, and P.Grünwald, On
Predictive Distributions and Bayesian Networks.
Statistics and Computing
10 (2000), 39-54
Bayesian methods in action
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Baldi, P., Brunak, S.
Bioinformatics: the Machine Learning Approach (2nd edition).
MIT Press, 1998.
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Evett,I., Weir, B.S., Interpreting DNA evidenceSinauer
Associates, inc, 1998.
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Ruohotie,R., Nokelainen,P.,Tirri,H.,Silander,T. Modeling Individual
and Organizational Prerequisites of Professional Growth - Papers
presented at International Conferences 1999-2001 .
Saarijärven Offset, 2001.
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B-Course, version 2.0.0
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