Probability theory for the brier game

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

Volumn 1316, Issue , 1997, Pages 323-338

  Vovk, V ^a  

^a UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; PROBABILITY;

GOOD EXPERTS; LAW OF LARGE NUMBERS; LAW OF THE ITERATED LOGARITHM; LIMIT THEOREM; PREDICTION WITH EXPERT ADVICE; PROBABILITY THEORY;

GAME THEORY;

EID: 84958055998     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-63577-7_52     Document Type: Conference Paper

References (19)

1. P. Billingsley, Probability and measure, New York: Wiley, 1979.
2. N. Cesa-Bianchi, P. Long, and M. K. Warmuth, Worst-case quadratic loss bounds for on-line prediction of linear functions by gradient descent, IEEE Trans. Neural Networks, 7 (1996), 604-619.
3. T. Cover and E. Ordentlich, Universal portfolios with side information, IEEE Trans. Inform. Theory, 42 (1996), No. 2.
4. A. P. Dawid, Calibration-based empirical probability (with discussion), Ann. Statist., 13 (1985), 1251-1285.
5. A. P. Dawid and V. G. Vovk, Prequential probability: principles and properties, submitted to Bernoulli.
6. A. DeSantis, G. Markowsky, and M. N. Wegman, Learning probabilistic prediction functions, in "Proceedings, 29th Annual IEEE Symposium on Foundations of Computer Science", pp. 110-119, Los Alamitos, CA: IEEE Comput. Soc., 1988.
7. D. Haussler, J. Kivinen, and M. K. Warmuth, Tight worst-case loss bounds for predicting with expert advice, Technical Report UCSC-CRL-94-36, revised December 1994. Short version in "Computational Learning Theory" (P. Vitányi, Ed.), Lecture Notes in Computer Science, Vol. 904, pp. 69-83, Berlin: Springer, 1995.
8. M. Herbster and M. K. Warmuth, Tracking the best expert, in "Proceedings, 12th International Conference on Machine Learning", 1995.
9. J. Kivinea and M. K. Warmuth, Additive versus exponentiated gradient updates for linear prediction, Inform. Computation, 132 (1997), 1-64.
10. N. Littlestone, P. M. Long, and M. K. Warmuth, On-line learning of linear functions, J. Computational Complexity, 5 (1995), 1-23.
11. N. Littlestone and M. K. Warmuth, The Weighted Majority Algorithm, Inform. Computation, 108 (1994), 212-261.
12. G. Shafer, The art of causal conjecture, Cambridge, MA: MIT Press, 1996.
13. R. J. Solomonoff, Complexity-based induction systems: comparison and convergence theorems, IEEE Trans. Inform. Theory, IT-24 (1978), 422-432.
14. V. Vovk, On a randomness criterion. Soviet Math. Doklady 35 (1987), 656-660.
15. V. Vovk, Aggregating strategies, in "Proceedings, 3rd Annum Workshop on Computational Learning Theory" (M. Fulk and J. Case, Eds.), pp. 371-383, San Mateo, CA: Morgan Kaufmann, 1990.
16. V. Vovk, A logic of probability, with application to the foundations of statistics (with discussion), J. R. Statist. Soc. B, 55 (1993), 317-351.
17. V. Vovk, A game of prediction with expert advice, to appear in J. Comput. Inform. Syst. Short version in "Proceedings, 8th Annual ACM Conference on Computational Learning Theory", pp. 51-60, New York: Assoc. Comput. Mach., 1995.
18. V. Vovk, Derandomizing stochastic prediction strategies, in "Proceedings, 10th Annual ACM Conference on Computational Learning Theory", to appear.
19. V. Vovk, On-line competitive linear regression, Technical Report CSD-TR-97-13, Department of Computer Science, Royal Holloway, University of London, 1997.