Synthese 172 (1) (2010)
|Abstract||We suggest to define objective probabilities by similarity-weighted empirical frequencies, where more similar cases get a higher weight in the computation of frequencies. This formula is justified intuitively and axiomatically, but raises the question, which similarity function should be used? We propose to estimate the similarity function from the data, and thus obtain objective probabilities. We compare this definition to others, and attempt to delineate the scope of situations in which objective probabilities can be used.|
|Keywords||No keywords specified (fix it)|
|Categories||No categories specified (fix it)|
|Through your library||Configure|
Similar books and articles
Matthew Weiner & Nuel Belnap (2006). How Causal Probabilities Might Fit Into Our Objectively Indeterministic World. Synthese 149 (1):1--36.
John L. Pollock (2002). Causal Probability. Synthese 132 (1-2):143 - 185.
David E. Buschena & David Zilberman (1999). Testing the Effects of Similarity on Risky Choice: Implications for Violations of Expected Utility. Theory and Decision 46 (3):253-280.
Robert N. Brandon (1978). Evolution. Philosophy of Science 45 (1):96-109.
Jon Williamson, Motivating Objective Bayesianism: From Empirical Constraints to Objective Probabilities.
J. Ellenberg & E. Sober (2011). Objective Probabilities in Number Theory. Philosophia Mathematica 19 (3):308-322.
Sorry, there are not enough data points to plot this chart.
Added to index2009-04-20
Total downloads5 ( #160,171 of 548,951 )
Recent downloads (6 months)0
How can I increase my downloads?