The Probabilistic Mind: Prospects for Bayesian Cognitive ScienceThe rational analysis method, first proposed by John R. Anderson, has been enormously influential in helping us understand high-level cognitive processes. The Probabilistic Mind is a follow-up to the influential and highly cited 'Rational Models of Cognition' (OUP, 1998). It brings together developments in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian The Probabilistic Mind will be valuable for psychologists and philosophers interested in cognition. |
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a X The Expectation - Maximization algorithm A standard approach to solving the problem of estimating probability distributions involving latent variables from training data is the Expectation - Maximization ( EM ) algorithm ( Dempster ...
The task of the learning algorithm is to process sequences of observations in order to induce a hypothesis . The hypothesis space of the algorithm can also be viewed as a model class . A model is simply a parameterized family of ...
Computer science and statistics have developed useful algorithms for approximating intractable probability ... The local MAP algorithm Anderson ( 1990 , 1991 ) identified two desiderata for an approximate inference algorithm : that it ...
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Contents
prospects for a Bayesian cognitive science | 3 |
A primer on probabilistic inference | 33 |
Rational analyses instrumentalism and implementations | 59 |
Copyright | |
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Other editions - View all
The Probabilistic Mind: Prospects for Bayesian Cognitive Science Nick Chater,Mike Oaksford Limited preview - 2008 |
The Probabilistic Mind: Prospects for Bayesian Cognitive Science Nick Chater,Mike Oaksford No preview available - 2008 |