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Single Idea 16839

[filed under theme 14. Science / C. Induction / 6. Bayes's Theorem ]

Full Idea

Explanatory considerations may play an important role in the actual mechanisms by which inquirers 'realize' Bayesian reasoning.

Gist of Idea

Explanation may be an important part of implementing Bayes's Theorem

Source

Peter Lipton (Inference to the Best Explanation (2nd) [2004], 07 'The Bayesian')

Book Ref

Lipton,Peter: 'Inference to the Best Explanation (2nd ed)' [Routledge 2004], p.107


A Reaction

Lipton's strategy for making peace between IBE and Bayesians. Explanations give likeliness. The background question for Bayesians always seems to be how the initial probabilities are assigned. Pure logic won't do that job.


The 19 ideas with the same theme [equation showing probability of an inductive truth]:

The probability of two events is the first probability times the second probability assuming the first [Bayes]
Trying to assess probabilities by mere calculation is absurd and impossible [James]
Ramsey gave axioms for an uncertain agent to decide their preferences [Ramsey, by Davidson]
Instead of gambling, Jeffrey made the objects of Bayesian preference to be propositions [Jeffrey, by Davidson]
Probabilities can only be assessed relative to some evidence [Dancy,J]
Probability of H, given evidence E, is prob(H) x prob(E given H) / prob(E) [Horwich]
Bayes' theorem explains why very surprising predictions have a higher value as evidence [Horwich]
Bayes seems to rule out prior evidence, since that has a probability of one [Lipton]
Bayes is too liberal, since any logical consequence of a hypothesis confirms it [Lipton]
A hypothesis is confirmed if an unlikely prediction comes true [Lipton]
Bayes involves 'prior' probabilities, 'likelihood', 'posterior' probability, and 'conditionalising' [Lipton]
Explanation may be an important part of implementing Bayes's Theorem [Lipton]
Since every tautology has a probability of 1, should we believe all tautologies? [Pollock/Cruz]
Bayesian inference is forced to rely on approximations [Thagard]
Bayes produces weird results if the prior probabilities are bizarre [Sider]
Bayesianism claims to find rationality and truth in induction, and show how science works [Bird]
If the rules only concern changes of belief, and not the starting point, absurd views can look ratiional [Okasha]
Probability supports Bayesianism better as degrees of belief than as ratios of frequencies [Colyvan]
The Bayesian approach is explicitly subjective about probabilities [Reiss/Sprenger]