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Likelihood-Free Methods for Cognitive Science pp 115–117Cite as

Conclusions

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Part of the book series: Computational Approaches to Cognition and Perception ((CACP))

Abstract

In this chapter, we provide an outlook on likelihood-free methods and their potential impact on the field of cognitive science. We also discuss the role that mathematical tractability should play in the development of new computational models, given the development of feasible estimation techniques for simulation-based inference.

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Author information

Authors and Affiliations

  1. Department of Psychology, The Ohio State University, Columbus, OH, USA

    James J. Palestro, Per B. Sederberg, Trisha Van Zandt & Brandon M. Turner

  2. University of Melbourne, Parkville, VIC, Australia

    Adam F. Osth

Authors
  1. James J. Palestro
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  2. Per B. Sederberg
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  3. Adam F. Osth
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  4. Trisha Van Zandt
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  5. Brandon M. Turner
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Palestro, J.J., Sederberg, P.B., Osth, A.F., Zandt, T.V., Turner, B.M. (2018). Conclusions. In: Likelihood-Free Methods for Cognitive Science. Computational Approaches to Cognition and Perception. Springer, Cham. https://doi.org/10.1007/978-3-319-72425-6_6

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