"The definitive work on the Bayesian approach to Cognitive Science and an important work in understanding the mind and the brain"--Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Thomas L. Griffiths, Nick Chater, and Joshua B. Tenenbaum
Inhaltsangabe
Preface Part I: The Basics 1 Introducing the Bayesian approach to cognitive science 2 Probabilistic models of cognition in historical context 3 Bayesian inference 4 Graphical models 5 Building complex generative models 6 Approximate probabilistic inference 7 From probabilities to actions Part II: Advanced Topics 8 Learning inductive bias with hierarchical Bayesian models 9 Capturing the growth of knowledge with nonparametric Bayesian models 10 Estimating subjective probability distributions 11 Sampling as a bridge across levels of analysis 12 Bayesian models and neural networks 13 Resource-rational analysis 14 Theory of mind and inverse planning 15 Intuitive physics as probabilistic inference 16 Language processing and language learning 17 Bayesian inference over logical representations 18 Probabilistic programs as a unifying language of thought 19 Learning as Bayesian inference over programs 20 Bayesian models of cognitive development 21 The limits of inference and algorithmic probability 22 A Bayesian conversation Conclusion Acknowledgments References
Preface Part I: The Basics 1 Introducing the Bayesian approach to cognitive science 2 Probabilistic models of cognition in historical context 3 Bayesian inference 4 Graphical models 5 Building complex generative models 6 Approximate probabilistic inference 7 From probabilities to actions Part II: Advanced Topics 8 Learning inductive bias with hierarchical Bayesian models 9 Capturing the growth of knowledge with nonparametric Bayesian models 10 Estimating subjective probability distributions 11 Sampling as a bridge across levels of analysis 12 Bayesian models and neural networks 13 Resource-rational analysis 14 Theory of mind and inverse planning 15 Intuitive physics as probabilistic inference 16 Language processing and language learning 17 Bayesian inference over logical representations 18 Probabilistic programs as a unifying language of thought 19 Learning as Bayesian inference over programs 20 Bayesian models of cognitive development 21 The limits of inference and algorithmic probability 22 A Bayesian conversation Conclusion Acknowledgments References
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