nips nips2002 nips2002-157 nips2002-157-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Harald Steck, Tommi S. Jaakkola
Abstract: A common objective in learning a model from data is to recover its network structure, while the model parameters are of minor interest. For example, we may wish to recover regulatory networks from high-throughput data sources. In this paper we examine how Bayesian regularization using a product of independent Dirichlet priors over the model parameters affects the learned model structure in a domain with discrete variables. We show that a small scale parameter - often interpreted as
[1] W. Buntine. Theory refinement on Bayesian networks. Conference on Uncertainty in Artificial Intellig ence, pages 52- 60. Morgan Kaufmann, 1991.
[2] G. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309- 47, 1992.
[3] A. P. Dawid. Statistical theory. The prequential approach. Journal of the Royal Statistical Society, Series A , 147:277- 305, 1984.
[4] T. S. Ferguson. A Bayesian analysis of some nonparametric problems. Annals of Statistics, 1:209- 30, 1973.
[5] D. Heckerman. A tutorial on learning with Bayesian networks. In M. I. Jordan (Ed.), Learning in Graphical Models, pages 301- 54. Kluwer , 1996.
[6] D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 20:197243,1995.
[7] R. E . Kass and L. Wasserman. Formal rules for selecting prior distributions: a review and annotated bibliography. Technical Report 583, CMU, 1993.
[8] S. Petrone and A. E. Raftery. A note on the Dirichlet process prior in Bayesian nonparametric inference with partial exchangeability. Technical Report 297, University of Washington, Seattle, 1995.
[9] J. Sethuraman and R. C. Tiwari. Convergence of Dirichlet measures and the interpretation of their parameter. In S. S. Gupta and J. O. Berger (Eds.), Statistical Decision Theory and Related Topics III, pages 305- 15, 1982.
[10] W. Sewell and V. Shah. Social class, parental encouragement, and educational aspirations. American Journal of Sociology, 73:559- 72, 1968.
[11] M. Stone. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B, 36:111- 47, 1974.
[12] S. G. Walker and B. K. Mallick. A note on the scale parameter of the Dirichlet process. The Canadian Journal of Statistics, 25:473- 9, 1997.