Bibliography

1

Berger, J.O.: Statistical Decision Theory and Bayesian Analysis. New York: Springer Verlag, 1980.

2

Doob, J.L.: Stochastic Processes. New York: Wiley, 1953 (New edition 1990).

3

Everitt, B.S. & Hand, D.J.: Finite Mixture Distributions. Chapman & Hall, 1981.

4

Gelman A., Carlin, J.B., Stern, H.S., & Rubin, D.B.: Bayesian Data Analysis. New York: Chapman & Hall, 1995.

5

Girosi, F., Jones, M., & Poggio, T.: Regularization Theory and Neural Networks Architectures. Neural Computation 7 (2), 219-269, 1995.

6

Lemm, J.C.: Prior Information and Generalized Questions. A.I.Memo No. 1598, C.B.C.L. Paper No. 141, Massachusetts Institute of Technology, 1996. (available at http://pauli.uni-muenster.de/${}^\sim$lemm)

7

Lemm, J.C.: How to Implement A Priori Information: A Statistical Mechanics Approach. Technical Report MS-TP1-98-12, Universität Münster, 1998. (cond-mat/9808039, also available at http://pauli.uni-muenster.de/${}^\sim$lemm.)

8

Lemm, J.C.: Quadratic Concepts. In Niklasson, L, Boden, M, Ziemke, T.(eds.): Proceedings of the 8th International Conference on Artificial Neural Networks (ICANN 98), Skövde, Sweden, September 2-4, 1998, Springer Verlag, 1998.

9

Lemm, J.C.: Bayesian Field Theory. Technical Report MS-TP1-99-1, Universität Münster, 1999. (available at http://pauli.uni-muenster.de/${}^\sim$lemm.)

10

Lemm, J.C., Uhlig, J., & Weiguny, A.: A Bayesian Approach to Inverse Quantum Statistics. Technical Report MS-TP1-99-6, Universität Münster, 1999. (cond-mat/9907013, also available at http://pauli.uni-muenster.de/${}^\sim$lemm.)

11

Robert, C.P.: The Bayesian Choice. New York: Springer Verlag, 1994.

12

Sivia, D.S.: Data Analysis: A Bayesian Tutorial. Oxford: Oxford University Press, 1996.

13

Tikhonov A.N. & Arsenin V.: Solution of Ill-posed Problems. New York: Wiley, 1977.

14

Williams, C.K.I. & Rasmussen, C.E.: Gaussian processes for regression. In Proc. NIPS8, MIT Press, 1996.

15

Vapnik, V.N.: Estimation of dependencies based on empirical data. New York: Springer Verlag, 1982.

16

Vapnik, V.N.: Statistical Learning Theory. New York: Wiley, 1998.

17

Wahba, G.: Spline Models for Observational Data. Philadelphia: SIAM, 1990.

18

Whittaker, E.T., On a new method of graduation. Proc. Edinborough Math. Assoc., 78, 81-89, 1923.