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Bayesian Methods for the Analysis of Human Behaviour

  • Gwenn Englebienne

Abstract

The nature of human behavior makes Bayesian methods particularly well-suited for its automated analysis. It is complex, highly variable, frequently inconsistent and is often the consequence of thought processes that we know not of. Advanced modeling techniques that specifically take uncertainty into account are therefore highly desirable. If it were not for the associated computational cost, whether real or perceived, fully Bayesian methods would probably be dominating the field. It can therefore be expected that, as computational power becomes ever more easily and cheaply available, we will see a growing trend towards the wide use of these methods. This chapter introduces the basics of Bayesian methods, and focuses specifically on two major techniques which have received increasing attention in recent years, thanks to their flexibility, ease of use and computational tractability: Dirichlet processes and Gaussian processes.

This chapter introduces the basics of Bayesian methods, and focuses specifically on two major techniques which have received increasing attention in recent years, thanks to their flexibility, ease of use and computational tractability: Dirichlet processes and Gaussian processes.

Keywords

Kernel Function Bayesian Network Gaussian Process Bayesian Method Mixture Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.University of AmsterdamAmsterdamThe Netherlands

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