Scientific Reasoning: The Bayesian ApproachIn this clearly reasoned defense of Bayes's Theorem -- that probability can be used to reasonably justify scientific theories -- Colin Howson and Peter Urbach examine the way in which scientists appeal to probability arguments, and demonstrate that the classical approach to statistical inference is full of flaws. Arguing the case for the Bayesian method with little more than basic algebra, the authors show that it avoids the difficulties of the classical system. The book also refutes the major criticisms leveled against Bayesian logic, especially that it is too subjective. This newly updated edition of this classic textbook is also suitable for college courses. |
Contents
The Laws of Probability | 45 |
Contents | |
The Probability Calculus 13 | 13 |
Copyright | |
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algebra argued argument assignments assumption Bayes's theorem Bayesian approach Bayesian theory believe Chapter classical clinical trial conditional probability conditionalisation confidence interval confirmed consequences consider consistency constraints countable deductive logic defined density entails epistemic probability equal equation estimates evidence example experiment experimental fact fair betting quotients false Finetti finite Fisher formal given groups hence idea independent inductive inference interpretation intuitively Lakatos least squares likelihood mathematical mean measure method methodology Neyman null hypothesis objective probability observed odds outcome P(he parameter particular Popper population possible posterior distribution posterior probability predictions Principle of Indifference prior distribution prior probability prob proba probabilistic probability axioms probability calculus probability function problem problem of induction propositions Prout's hypothesis random sample random variables reason rejected relative frequency result Savage's scientific scientists sequence significance tests standard deviation statisticians statistics stopping rule Suppose tion true valid X₁