Empiricism and/or instrumentalism?

Erkenntnis

Volumn 79, Issue 5, 2014, Pages 1019-1041

  Bandyopadhyay, Prasanta S ^a   Greenwood, Mark ^a   Brittan, Gordon ^a   Aho, Ken A ^b  

^a MONTANA STATE UNIVERSITY   (United States)

^b Nucl Eng Idaho State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84908090236     PISSN: 01650106     EISSN: 15728420     Source Type: Journal    
DOI: 10.1007/s10670-013-9567-8     Document Type: Article

References (42)

1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), Second international symposium on information theory (pp. 267–281). Budapest: Akademia Kaido.
2. Bandyopadhyay, P. S. (1997). On an inconsistency in constructive empiricism. Philosophy of Science,64, 511–514.
3. Bandyopadhyay, P. S. (2007). Why Bayesianism? A primer on a probabilistic philosophy of science. In S. K. Upadhyay, U. Sing, & D. K. Dey (Eds.), Bayesian statistics and its applications (pp. 42–62). New Delhi: Anamaya Publishers.
4. Bandyopadhyay, P. S., & Boik, R. (1999). The curve-fitting problem: A Bayesian rejoinder. Philosophy of Science,66, S390–S402.
5. Bandyopadhyay, P. S., Boik, R., & Basu, P. (1996). The curve-fitting problem: A Bayesian approach. Philosophy of Science,63, S265–S272.
6. Bandyopadhyay, P. S., & Brittan, G., Jr. (2001). Logical consequence and beyond: A close look at model selection in statistics. In J. Woods & B. Hepburb (Eds.), Logical consequence (pp. 1–17). Oxford: Hermes Science Publishing Company.
7. Berger, J. (1985). Statistical decision theory and Bayesian analysis. New York: Springer.
8. Birnbaum, A. (1962). On the foundations of statistical inference. Journal of the American Statistical Association,57, 269–306.
9. Boik, R. (2004). Commentary. In M. Taper & S. Lele (Eds.), The nature of scientific evidence (pp. 167–180). Chicago: The University of Chicago Press.
10. Box, G. (1979). Robustness in scientific model building. In R. I. Launer & G. N. Wilkinson (Eds.), Robustness in statistics (pp. 201–236). New York: Academic Press.
11. Boyd, R. (1985). Observations, explanatory power, and simplicity. In P. Achinstein & O. Hannaway (Eds.), Observation, experiment, and hypothesis in modern physical science. Cambridge, MA: MIT Press.
12. Burnham, K., & Anderson, D. (1998). Model selection and inference. New York: Springer.
13. Cassella, G., & Berger, R. (1990). Statistical Inference. California: Wadsworth & Brooks.
14. Eells, E. (1993). Probability, inference, and decision. In J. Fetzer (Ed.), Foundations of philosophy of science (pp. 192–208). New York: Paragon House.
15. Evans, M. (2013). What does the proof of Birnbaum’s theorem prove? Electronic Journal of Statistics, 1–9.
16. Forster, M., & Sober, E. (1994). How to tell when simpler, more unified, or less ad hoc theories will provide more accurate predictions. British Journal for the Philosophy of Science,45, 1–35.
17. Forster, M., & Sober, E. (2004a). Why likelihood? In M. Taper & S. Lele (Eds.), The nature of scientific evidence. Chicago: University of Chicago Press.
18. Forster, M., & Sober, E. (2004b). Rejoinder to why likelihood? In M. Taper & S. Lele (Eds.), The nature of scientific evidence. Chicago: University of Chicago Press.
19. Godfrey-Smith, P. (1999). Procrustes probably. Philosophical Studies,95, 175–180.
20. Good, I. J. (1983). The white shoe is a red herring. First published in BJPS, 1967, 17: p. 332. Reprinted in good thinking. Minnesota: University of Minnesota Press.
21. Hacking, I. (1965). Logic of statistical inference. Cambridge: Cambridge University Press.
22. Hanna, J. (2004). Contra Ladyman: What really is right with constructive Empiricism. British Journal for the Philosophy of Science,55, 767–777.
23. Howson, C., & Urbach, P. (2006). Scientific reasoning: The Bayesian approach (3rd ed.). La Salle, IL: Open Court.
24. Jeffreys, W., & Berger, J. (1992). Ockham’s Razor and Bayesian analysis. American Scientists,80, 64–72.
25. Kitcher, P. (1993). The advancement of science. New York: Oxford University Press.
26. Kitcher, P. (2001). Science, truth, and democracy. Oxford: Oxford University Press.
27. Kullback, S., & Leibler, R. (1951). On information and sufficiency. Annals of Mathematical Statistics,22, 79–86.
28. Ladyman, J. (2000). What’s really wrong with constructive Empiricism? Van Fraassen and the metaphysics of modality. British Journal for the Philosophy of Science,51, 837–856.
29. Miller, R. (1987). Explanation, confirmation, and reality in the natural and social sciences. Princeton: Princeton University Press.
30. Pawitan, Y. (2001). All likelihood: Statistical modelling and inference using likelihood. Oxford: Oxford University Press.
31. Roberts, C. (1994). The Bayesian choice. New York: Springer.
32. Royall, R. (1997). Statistical evidence. New York: Chapman & Hall.
33. Sober, E. (1988). Reconstructing the past: Parsimony, evolution, and inference. Cambridge: MIT Press.
34. Sober, E. (1993). Epistemology for empiricists. In P. French, T. Uehling, & H. Weinstein (Eds.), Midwest studies in philosophy, XVIII.
35. Sober, E. (1995). From a biological point of view. Cambridge: Cambridge University Press.
36. Sober, E. (1996). Parsimony and predictive equivalence. Erkenntnis,44, 167–197.
37. Sober, E. (1998). Instrumentalism revisited. Critica,31, 3–38.
38. Sober, E. (2001). Instrumentalism, Parsimony, and the Akaike framework. Philosophy of science (Proceedings), Fall.
39. Sober, E. (2008). Evidence and evolution: The logic behind the science. Cambridge: Cambridge University Press.
40. Swinburne, R. (2001). Epistemic justification. Oxford: Clarendon Press.
41. Van Fraassen, B. (1980). The scientific image. Oxford: Oxford University Press.
42. Van Fraassen, B. (1989). Laws and symmetry. Oxford: Oxford University Press.