Bibliography

1

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

2

Good, I.J. & Gaskins, R.A. (1971) Nonparametric roughness penalties for probability densities. Biometrika 58, 255-277.

3

Lemm, J.C. (1999) Mixtures of Gaussian Process Priors. In Proceedings of the Ninth International Conference on Artificial Neural Networks (ICANN99), IEEE Conference Publication No. 470. London: Institution of Electrical Engineers.

4

Lemm, J.C. (1999) Bayesian Field Theory. Nonparametric approaches to density estimation, regression, classification, and inverse quantum problems. Technical Report, Münster University, MS-TP1-99-1. arXiv: physics/9911077.

5

Lemm, J.C., Uhlig J.,& Weiguny, A. (1999) A Bayesian Approach to Inverse Quantum Statistics. Technical Report, MS-TP1-99-6, Münster University, (submitted to Phys. Rev. Lett.) arXiv:cond-mat/9907013.

6

MacKay, D.J.C. (1994) Hyperparameters: optimise or integrate out? In Heidbreder, G. (ed.) Maximum Entropy and Bayesian Methods, Santa Barbara 1993. Dordrecht: Kluwer.

7

Montvay, I. & Münster, G. (1994) Quantum Fields on a Lattice. Cambridge: Cambridge University Press.

8

Rose, K., Gurewitz, E., & Fox, G.C. (1990) Statistical mechanics and phase transitions in clustering. Phys. Rev. Lett. 65, 945-948.

9

Silverman, B.W. (1986) Density Estimation for Statistics and Data Analysis. London: Chapman & Hall.

10

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

11

Williams, C.K.I. & Barber, D. (1998) Bayesian Classification With Gaussian Processes IEEE Trans. on Pattern Analysis and Machine Intelligence. 20(12), 1342-1351.

12

Williams, C.K.I. & Rasmussen, C.E. (1996) Gaussian Processes for Regression. in Advances in Neural Information Processing Systems 8, D.S. Touretzky et al (eds.), 515-520, Cambridge, MA: MIT Press.