Statistics between inductive logic and empirical science

Journal of Applied Logic

Volumn 7, Issue 2, 2009, Pages 239-250

  Sprenger, Jan ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Bayesian inference; Inductive logic; Model selection; Parameter estimation; Statistics

Indexed keywords

BAYESIAN INFERENCE; COMPLEX PROBLEMS; DECISION-THEORETIC; INDUCTIVE LOGIC; MATHEMATICAL THEORIES; MODEL SELECTION; OBSERVATIONAL DATUM; STATISTICAL INFERENCES; THEORETICAL MODELS;

BAYESIAN NETWORKS; INFERENCE ENGINES; METAL ANALYSIS; PARAMETER ESTIMATION;

STATISTICS;

EID: 64149131676     PISSN: 15708683     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jal.2007.11.007     Document Type: Article

References (37)

1. Akaike H. Information theory as an extension of the maximum likelihood principle. In: Petrov B.N., and Csaki F. (Eds). Second International Symposium on Information Theory (1973), Akademiai Kiado, Budapest 267-281
2. Berger J.O., and Perrichi L.R. The intrinsic Bayes factor for model selection and prediction. Journal of the American Statistical Association 91 (1996) 109-122
3. Berger J.O., Boukai B., and Wang Y. Unified Bayesian and frequentist testing of a precise hypothesis (with discussion). Statistical Science 12 (1997) 133-160
4. Berger J.O. Could Fisher, Jeffreys and Neyman have agreed on testing? (with discussion). Statistical Science 18 (2003) 1-32
5. Bozdogan H. ICOMP: A new model selection criterion. In: Bock H.H. (Ed). Classification and Related Methods of Data Analysis (1988), Elsevier Science, Amsterdam 599-608
6. Bozdogan H. Akaike's information criterion and recent developments in information complexity. Journal of Mathematical Psychology 44 (2000) 62-91
7. Burnham K.P., and Anderson D.R. Model Selection and Inference: A Practical Information-Theoretic Approach (1998), Springer, New York
8. Carnap R. Logical Foundations of Probability. second ed (1962), The University of Chicago Press, Chicago
9. Chatfield C. Model uncertainty, data mining and statistical inference (with discussion). Journal of the Royal Statistical Society, Series A 158 (1995) 419-466
10. Cox D. Principles of Statistical Inference (2006), Cambridge University Press, Cambridge
11. Festa R. A measure for the distance between an interval hypothesis and the truth. Synthese 67 (1986) 273-320
12. Festa R. Optimum Inductive Methods (1993), Kluwer, Dordrecht
13. Fitelson B. Inductive logic. In: Pfeifer J., and Sarkar S. (Eds). Philosophy of Science: An Encyclopedia (2005), Routledge, London
14. Forster M. Key concepts in model selection: Performance and generalizability. Journal of Mathematical Psychology 44 (2000) 205-231
15. Forster M., and Sober E. How to tell when simpler, more unified or less ad hoc theories provide more accurate predictions. British Journal for the Philosophy of Science 45 (1994) 1-35
16. Gaifman H., and Snir M. Probabilities over rich languages. Journal of Symbolic Logic 47 (1982) 495-548
17. R. Haenni, J.-W. Romeijn, G. Wheeler, J. Williamson, Possible semantics for a common framework of probabilistic logics, in: V.N. Huynh (Ed.), Proceedings of the International Workshop on Interval/Probabilistic Uncertainty and Non-Classical Logics, Advances in Soft Computing, Ishikawa, 2008
18. Hailperin T. Sentential Probability Logic: Origins Development, Current Status and Technical Applications (1996), Lehigh University Press, Bethlehem/PA
19. Hawthorne J. Inductive logic. in: Encyclopedia of Philosophy (2005). http://plato.stanford.edu/entries/logic-inductive
20. Hintikka J. A two-dimensional continuum of inductive methods. In: Hintikka J., and Suppes P. (Eds). Aspects of Inductive Logic (1966), North-Holland, Amsterdam 113-132
21. Hoeting J., Madigan D., Raftery A., and Volinsky C. Bayesian model averaging: A tutorial. Statistical Science 14 (1999) 382-417
22. Howson C. Hume's Problem (2003), Oxford University Press, Oxford
23. Howson C., and Urbach P. Scientific Reasoning: The Bayesian Approach. third ed. (2005), Open Court, La Salle
24. Kass R., and Raftery A. Bayes factors. Journal of the American Statistical Association 90 (1995) 773-790
25. Kullback S., and Leibler R.A. On information and sufficiency. Annals of Mathematical Statistics 22 (1951) 79-86
26. Myung J. The importance of complexity in model selection. Journal of Mathematical Psychology 44 (2000) 190-204
27. Romeijn J.-W. Hypotheses and inductive predictions. Synthese 141 (2004) 333-364
28. Romeijn J.-W. Theory change and Bayesian statistical inference. Philosophy of Science 72 (2005) 1174-1186
29. Roulston M.S., Kaplan D.T., Hardenberg J., and Smith L.A. Using medium-range weather forecasts to improve the value of wind energy production. Renewable Energy 28 (2003) 585-602
30. Schwarz G. Estimating the dimension of a model. Annals of Statistics 6 (1978) 461-464
31. Shannon C., and Weaver W. The Mathematical Theory of Communication (1949), University of Illinois Press, Urbana
32. Smith L. Predictability past, predictability present. Proceedings on the ECMWF Seminar on Predictability (2003), ECMWF, Reading 219-242
33. Takeuchi K. Distribution of information statistics and a criterion of model fitting. Suri-Kagaku (Mathematical Sciences) 153 (1976) 12-18
34. Taper M. Model identification from many candidates. In: Taper M., and Lele S. (Eds). The Nature of Scientific Evidence (2004), The University of Chicago Press, Chicago, London 488-524 (with discussion)
35. Wasserman L. Bayesian model selection and model averaging. Journal of Mathematical Psychology 44 (2000) 92-107
36. Williamson J. Motivating objective Bayesianism: from empirical constraints to objective probabilities. In: Harper W., and Wheeler G. (Eds). Probability and Inference: Essays in Honour of Henry E. Kyburg Jr. (2007), College Publications, London 151-179
37. Zucchini W. An introduction to model selection. Journal of Mathematical Psychology 44 (2000) 41-61