David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Synthese 20 (3):371 - 387 (1969)
A comparison of Neyman's theory of interval estimation with the corresponding subjective Bayesian theory of credible intervals shows that the Bayesian approach to the estimation of statistical parameters allows experimental procedures which, from the orthodox objective viewpoint, are clearly biased and clearly inadmissible. This demonstrated methodological difference focuses attention on the key difference in the two general theories, namely, that the orthodox theory is supposed to provide a known average frequency of successful estimates, whereas the Bayesian account provides only a coherent ordering of degrees of belief and a subsequent maximization of subjective expected utilities. To rebut the charge of allowing biased procedures, the Bayesian must attack the foundations of orthodox, objectivist methods. Two apparently popular avenues of attack are briefly considered and found wanting. The first is that orthodox methods fail to apply to the single case. The second is that orthodox methods are subject to a typical Humean regress. The conclusion is that orthodox objectivist methods remain viable in the face of the subjective Bayesian alternative — at least with respect to the problem of statistical estimation.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Ian Hacking (1967). Slightly More Realistic Personal Probability. Philosophy of Science 34 (4):311-325.
Richard Jeffrey (1983). The Logic of Decision. University of Chicago Press.
John G. Kemeny (1955). Fair Bets and Inductive Probabilities. Journal of Symbolic Logic 20 (3):263-273.
Isaac Levi (1967/1973). Gambling with Truth. Cambridge,Mit Press.
Karl R. Popper (1959). The Propensity Interpretation of Probability. British Journal for the Philosophy of Science 10 (37):25-42.
Citations of this work BETA
No citations found.
Similar books and articles
Cory Juhl (1993). Bayesianism and Reliable Scientific Inquiry. Philosophy of Science 60 (2):302-319.
Persi Diaconis & Susan Holmes (1996). Are There Still Things to Do in Bayesian Statistics? Erkenntnis 45 (2-3):145 - 158.
Gregor Betz (2008). Evaluating Dialectical Structures with Bayesian Methods. Synthese 163 (1):25 - 44.
Jukka Corander & Pekka Marttinen (2006). Bayesian Model Learning Based on Predictive Entropy. Journal of Logic, Language and Information 15 (1-2):5-20.
Festa, Roberto, Optimum Inductive Methods. A Study in Inductive Probability, Bayesian Statistics, and Verisimilitude.
Daniel Steel (2003). A Bayesian Way to Make Stopping Rules Matter. Erkenntnis 58 (2):213--227.
Robert A. M. Gregson (1998). Understanding Bayesian Procedures. Behavioral and Brain Sciences 21 (2):201-202.
Ronald N. Giere (1970). An Orthodox Statistical Resolution of the Paradox of Confirmation. Philosophy of Science 37 (3):354-362.
Daniel Steel (2001). Bayesian Statistics in Radiocarbon Calibration. Proceedings of the Philosophy of Science Association 2001 (3):S153-.
Added to index2009-01-28
Total downloads14 ( #163,935 of 1,696,592 )
Recent downloads (6 months)2 ( #250,163 of 1,696,592 )
How can I increase my downloads?