
PhD Course on Bayesian Inference, MaySept 2011
Organized by Tomas Olofsson.
Bayesian inference or Bayesian probability theory deviates from
classical probability theory
in the way probabilities are interpreted. In Bayesian inference the
probabilities are interpreted as degrees of belief
rather than as an expected frequency of occurence of a repeated expriment,
which is the interpretation in classical probability theory.
(The classical frequency interpretation occurs merely as a special case
of the more general Bayesian interpretation.)
One consequence of this is that we by using the Bayesian definition of
probability will get a unified framwork for
probability theory and statistical inference.
Course Book: E.T. Jaynes: Probability Theory  The Logic of Science.
Cambridge University Press, 2003.
Errata
Course details (pdf)
Exercises:
Schedule:
 May 20, 1517.
 May 27, 1517. Responsible Annea. Presentation chapter 1.
 June 8, 1517. Rikke. Presentation chapter 2.
 June 14, 1517. Christofer. Presentation chapter 3.
 June 22, 1517. Pierre
 Aug 19, 1517. Simon (chapter 6.06.10).
 Aug 26, 1315. cancelled
 Sept 2, 1517. Adrian (remaining chapter 6). Note the time!
 Sept 9, 1315. Frederik. Presentation chapter 11.
 Sept 16, 1315. Tomas (MCMC, see Additional Material below.)
Additional Material:
Links to further reading:
 Probability Theory As Extended Logic. A site with many of Jaynes' articles and links to other Bayesian work. The site is maintained by Larry Bretthorst, one of Jaynes' former PhD students.
 D.S Sivia, "Data Analysis  A Bayesian Tutorial". This small book gives an easytoread introduction to probability theory as an extension to logic, and can serve as a companion to Jaynes' book.
 A. Zellner, "Introduction to Bayesian Inference in Economics". A very good complement to Jaynes, contains many useful results in time series analysis and control.
 H. Jeffreys, "Theory of Probability". The book that inspired Jaynes. Is more or less superseded by Jaynes' book today, although it still contains several useful derivations and comments.
 L. Bretthorst, Bayesian Spectrum Analysis and Parameter Estimation". Shows that the periodogram of a data series is a sufficient statistic for estimating the location of a single sinusoid in additive noise when the first and second moments of the disturbances are fixed. The resulting Bayesian spectrum analysis method is shown to outperform the periodogram by an order of magnitude in terms of estimation accuracy. Bretthorst then extends the simple frequency estimation problem to parameter estimation for a general linear model.
