The Probabilistic Mind: Prospects for Bayesian Cognitive ScienceNick Chater, Mike Oaksford The 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 methods. It synthesizes and evaluates the progress in the past decade, taking into account developments in Bayesian statistics, statistical analysis of the cognitive 'environment' and a variety of theoretical and experimental lines of research. The scope of the book is broad, covering important recent work in reasoning, decision making, categorization, and memory. Including chapters from many of the leading figures in this field, The Probabilistic Mind will be valuable for psychologists and philosophers interested in cognition. |
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Page 39
... distribution of a well known form , being a beta distribution with para- meters NH + 1 and NT + 1 , denoted Beta ( NÅ + 1 , NŢ + 1 ) ( e.g. , Pitman , 1993 ) . Using this prior , the MAP estimate for O is the same as the maximum ...
... distribution of a well known form , being a beta distribution with para- meters NH + 1 and NT + 1 , denoted Beta ( NÅ + 1 , NŢ + 1 ) ( e.g. , Pitman , 1993 ) . Using this prior , the MAP estimate for O is the same as the maximum ...
Page 50
... distribution is the distribution from which we want to generate samples . Since these methods are designed for arbitrary probability distributions , we will stop differentiating between observed and latent variables , and just treat the ...
... distribution is the distribution from which we want to generate samples . Since these methods are designed for arbitrary probability distributions , we will stop differentiating between observed and latent variables , and just treat the ...
Page 338
... distribution over topics . Then , for each word in that document , one chooses a topic at random according to this distribution , and draws a word from that topic . To introduce notation , we will write P ( z | d ) for the multinomial ...
... distribution over topics . Then , for each word in that document , one chooses a topic at random according to this distribution , and draws a word from that topic . To introduce notation , we will write P ( z | d ) for the multinomial ...
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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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 |
Common terms and phrases
algorithm alternative analysis approach approximate argument associated assumed assumption attribute Bayesian behavior beliefs Cambridge causal cause Chater choice cluster cognitive complexity computational concept conditional consider correlation decision depends described developed distribution effect environment estimate et al evidence example expected experience experimental explain framing function given heuristic human hypothesis important individual inference involved Journal judgment language learning logic mean memory methods natural normative Oaksford objects observed optimal options outcomes parameters participants particular performance possible posterior predictions present Press principle prior probabilistic probability problem produce prospect Psychological question rational rational analysis reasoning reference relation relative represent representation require response Review rule sample Science selection semantic shows similar simple statistical structure subjective suggest task theory tion trials University utility variables weight