Universal bayesian inference?
Behavioral and Brain Sciences 24 (4):662-663 (2001)
| Abstract | We criticise Shepard's notions of “invariance” and “universality,” and the incorporation of Shepard's work on inference into the general framework of his paper. We then criticise Tenenbaum and Griffiths' account of Shepard (1987b), including the attributed likelihood function, and the assumption of “weak sampling.” Finally, we endorse Barlow's suggestion that minimum message length (MML) theory has useful things to say about the Bayesian inference problems discussed by Shepard and Tenenbaum and Griffiths. [Barlow; Shepard; Tenenbaum & Griffiths]. | |||||||||
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Karl H. Pribram (2001). Shepard's Pie: The Other Half. Behavioral and Brain Sciences 24 (4):700-700.
Miklós Rédei (1992). When Can Non‐Commutative Statistical Inference Be Bayesian? International Studies in the Philosophy of Science 6 (2):129-132.
Miklós Rédei (1992). When Can Non-Commutative Statistical Inference Be Bayesian? International Studies in the Philosophy of Science 6 (2):129 – 132.
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Joshua B. Tenenbaum & Thomas L. Griffiths (2001). Generalization, Similarity, and Bayesian Inference. Behavioral and Brain Sciences 24 (4):629-640.
Nick Chater, Paul M. B. Vitányi & Neil Stewart (2001). Universal Generalization and Universal Inter-Item Confusability. Behavioral and Brain Sciences 24 (4):659-660.
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