Personal probabilities of probabilities

By definition, the subjective probability distribution of a random event is revealed by the (‘rational’) subject's choice between bets — a view expressed by F. Ramsey, B. De Finetti, L. J. Savage and traceable to E. Borel and, it can be argued, to T. Bayes. Since hypotheses are not observable events, no bet can be made, and paid off, on a hypothesis. The subjective probability distribution of hypotheses (or of a parameter, as in the current ‘Bayesian’ statistical literature) is therefore a figure of speech, an ‘as if’, justifiable in the limit. Given a long sequence of previous observations, the subjective posterior probabilities of events still to be observed are derived by using a mathematical expression that would approximate the subjective probability distribution of hypotheses, if these could be bet on. This position was taken by most, but not all, respondents to a ‘Round Robin’ initiated by J. Marschak after M. H. De-Groot's talk on Stopping Rules presented at the UCLA Interdisciplinary Colloquium on Mathematics in Behavioral Sciences. Other participants: K. Borch, H. Chernoif, R. Dorfman, W. Edwards, T. S. Ferguson, G. Graves, K. Miyasawa, P. Randolph, L. J. Savage, R. Schlaifer, R. L. Winkler. Attention is also drawn to K. Borch's article in this issue.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43847/1/11238_2004_Article_BF00169102.pd

Some history of the hierarchical Bayesian methodology

A standard tecnique in subjective Bayesian methodology is for a subject (you) to make judgements of the probabilities that a physical probability lies in various intervals. In the Bayesian hierarchical technique you make probability judgements (of a higher type, order, level or stage) concerning the judgements of lower type. The paper will outline some of the history of this hierarchical technique with emphasis on the contributions by I. J. Good because I have read every word written by hi

Bayesian non-parametric theory: discussion

Discussion on the paper by Dalal, Sid R., Nonparametric Bayes decision theory, part of a round table on Bayesian non-parametric theory held in the First International Congress on Bayesian Methods (Valencia, Spain, 28 May - 2 June 1979

John R. Lucas against Mechanism

Can the human mind be properly described in mechanical terms? It is in order to demonstrate that it cannot be that in 1959 John R. Lucas presented an anti-mechanist argument by appealing to G\uf6del\u2019s first incompleteness theorem. He attempted to show that any computational device cannot be an adequate model of the human mind, since, if there is a model of the human mind that is a machine, then there is at least a sentence that the machine cannot prove, while the human mind can. Lucas could not have foreseen the many disputes that this argument would have produced since then and the significant impact that it would have on the studies on the mechanical simulation of the human mind. As a tribute to the ingenuity of Lucas, this volume collects the most relevant papers that have contributed to the lively and stimulating debate arising from his argument