Some resources for matrix derivation and Normal distributions:
Lecture 3: A light article on Bayes is The Economist. London: Jan 7, 2006. Vol. 378, Iss. 8459; p. 73. On a machine in hut.fi-domain, go to The Economist
The Dutch Book Argument shows that priors must be probabilities unless one accepts irrational decisions. More details are in Notes by D. Freedman
More on Cox's Axioms can be found in K. S. Van Horn, Constructing a logic of plausible inference: a guide to Cox's Theorem, International Journal of Approximate Reasoning 34, no. 1 (Sept. 2003), pp. 3-24. Citeseer
Lecture 4: The demo illustrating overfitting avoidance is based on a generalized linear model with Normal likelihood and a Normal prior. Details can be found in C.K.I. Williams, Prediction with Gaussian Processes, Learning in Graphical Models, ed. Michael I. Jordan, MIT Press, 1999.
Lecture 6: Some background on the example illustrating a nonuniform prior for scale data: Benford's Law
Lecture 8: Simulation methods are a very large and active topic: the lecture only introduced the basic ideas. Introductions to MCMC include
Knowing when a simulation has converged is a major difficulty in
simulation methods. Perfect Sampling
offers an interesting way of overcoming this issue in certain type of situations.
Lecture 9: An example of applying variational approximation is found at Fergus et al., Removing Camera Shake From a Single Image
Lecture 10: Some resources on the EM algorithm:
Lecture 13: Making Hard Decisions: an Introduction to Decision Analysis, Robert T. Clemen, Duxbury Press, 1995, is a gentle introduction to decision analysis.
For an example where costs do matter in model selection, see Stochastic optimization methods for cost-effective quality assessment in health, D. Fouskakis and D. Draper, 2005. (submitted article)
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