Probability Objectivity And Evidence

Probability, Objectivity, and Evidence

Probability, Objectivity and Evidence

First published in 1984, in Probability, Objectivity and Evidence the author claims that the theory of probability provides a single, correct, analysis of probability and that the concept of probability employed in science can best be understood as that of inductive probability; to do so, it is necessary to show both how the logical relation theory of probability can be given a formulation sufficiently objective for the purposes of science, and how other attempts to explain the objective character of probability judgements are unsatisfactory. These and related questions occupy the first five chapters of the book. The last two chapters contain more or less independent material on the principle of indifference. The author argues that in essence, the logical relation theory alone can explain how we have objective knowledge of probabilities, and so it alone provides a viable system translation of the concept of probability used in science. This is a must read for students of logic and philosophy.

In Defence of Objective Bayesianism

How strongly should you believe the various propositions that you can express? That is the key question facing Bayesian epistemology. Subjective Bayesians hold that it is largely (though not entirely) up to the agent as to which degrees of belief to adopt. Objective Bayesians, on the other hand, maintain that appropriate degrees of belief are largely (though not entirely) determined by the agent's evidence. This book states and defends a version of objective Bayesian epistemology. According to this version, objective Bayesianism is characterized by three norms: · Probability - degrees of belief should be probabilities · Calibration - they should be calibrated with evidence · Equivocation - they should otherwise equivocate between basic outcomes Objective Bayesianism has been challenged on a number of different fronts. For example, some claim it is poorly motivated, or fails to handle qualitative evidence, or yields counter-intuitive degrees of belief after updating, or suffers from a failure to learn from experience. It has also been accused of being computationally intractable, susceptible to paradox, language dependent, and of not being objective enough. Especially suitable for graduates or researchers in philosophy of science, foundations of statistics and artificial intelligence, the book argues that these criticisms can be met and that objective Bayesianism is a promising theory with an exciting agenda for further research.