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Probability Disassembled

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Presentation on theme: "Probability Disassembled"— Presentation transcript:

1 Probability Disassembled
John D. Norton Department of History and Philosophy of Science University of Pittsburgh

2

3 How should we use axiom systems for the probability calculus?

4 Prix Fixe

5 À la carte

6 First English edition of Euclid’s Elements

7 Theorems of Euclid’s geometry. Euclid’s postulates
1. To draw a straight line from any point to any point. 2. To produce a finite straight line continuously in a straight line. 3. To describe a circle with any center and distance. 4. That all right angles are equal to one another. 5ONE. Through any given point can be drawn exactly one straight line parallel to a given line. Euclid’s postulates 5NONE. Through any given point NO straight lines can be drawn parallel to a given line. Theorems of spherical geometry. 5MORE. Through any given point MORE than one straight line can be drawn parallel to a given line. Theorems of hyperbolic geometry. Theorems of Euclid’s geometry.

8 An axiom system tailored to be used à la carte
Framework Addition Bayes Property • Narrowness • “Rescale and refute” Real Values Independent and qualitative

9 Framework

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11 Universal Comparability
[A|B] < [C|D] or [A|B] = [C|D] or [A|B] > [C|D] Fails for interval valued representations. 0.1 0.5 0.4 0.6 0.9 1 This atom of Radium 221 decayed within 30 seconds Radium 221 has a half life of 30 seconds. [ | ] Fails for very different outcome spaces. versus

12 Addition

13 Underlying intuition The range of degrees of confirmation span justification of complete belief and complete disbelief. Support for A Support for not-A Ignorance is precluded. Bel(A) = Bel(not-A) = 0 Bel(A or not-A) = 1

14 Bayes Property

15 Underlying intuition H E H E P(H&E|E) = P(H|E) Narrowness
An hypothesis H accrues inductive support from evidence E just if it has a disjunctive part that entails the evidence. H E Narrowness The presence of other disjunctive parts logically incompatible with the evidence does not affect the level of support. H E P(H&E|E) = P(H|E) ‘Refute and Rescale’…

16 Specific Conditioning Logic
[ ] > [ ] | | Canary or whale Canary Bird Bird

17 Underlying intuition = ‘Refute and Rescale’
Evidence bears on hypotheses H1, H2 that entail it by • refuting those logically incompatible with it H3 and • uniformly redistributing support over those that remain; this uniform redistribution is carried out everywhere in the same way and preserves the relative ranking of hypotheses that entail the evidence. H3 H1 H2 E • Inductive content always provided by priors. • No Bayesian computation is inductively complete. P(H1|E) P(E|H1) x P(H1) P(H1) P(H2|E) P(E|H2) x P(H2) P(H2) = 1 Elsewhere: All calculi of inductive inference, Bayesian or otherwise, are incomplete.

18 Real Values

19 Strengths are… … Real valued. Interval valued. Multidimensional.
So far, all properties are qualitative. Real valued. Interval valued. Multidimensional. Real and infinitesimal valued. Just leave as qualitative since sometimes support is imprecise and cannot be captured quantitatively.


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