Bayesian inference: An introduction to principles and practice in machine learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3176, Issue , 2004, Pages 41-62

  Tipping, Michael E ^a  

^a MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; INFERENCE ENGINES; TURING MACHINES;

BAYESIAN INFERENCE; MARGINALISATION; PRINCIPLES AND PRACTICES; RELEVANCE VECTOR MACHINE; SPARSE BAYESIAN;

MACHINE LEARNING;

EID: 35048822888     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-28650-9_3     Document Type: Article

References (14)

1. Jaynes, E.T.: Probability theory: the logic of science. Cambridge University Press (2003)
2. Evans, M., Swartz, T.B.: Methods for approximating integrals in statistics with special emphasis on Bayesian integration. Statistical Science 10 (1995) 254-272
3. Deal, M., Ghahramani, Z.: The Variational Bayes web site at http: //www.variational-bayes.org/ (2003)
4. Bishop, C.M., Tipping, M.E.: Variational relevance vector machines. In Boutilier, G., Goldszmidt, M., eds.: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann (2000) 46-53
5. Neal, R.M.: Bayesian Learning for Neural Networks. Springer (1996)
6. Berger, J.O.: Statistical decision theory and Bayesian analysis. Second edn. Springer (1985)
7. MacKay, D.J.C.: The evidence framework applied to classification networks. Neural Computation 4 (1992) 720-736
8. Williams, P.M.: Bayesian regularisation and pruning using a Laplace prior. Neural Computation 7 (1995) 117-143
9. Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learing Research 1 (2001) 211-244
10. Tipping, M.E., Faul, A.C.: Fast marginal likelihood maximisation for sparse Bayesian models. In Bishop, C.M., Frey, B.J., eds.: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6. (2003)
11. MacKay, D.J.C.: Bayesian interpolation. Neural Computation 4 (1992) 415-447
12. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)
13. Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis. Chapman & Hall (1995)
14. MacKay, D.J.C.: Information Theory, Inference and Learning Algorithms. Cambridge University Press (2003)