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Likelihood-Free Algorithms

  • James J. Palestro
  • Per B. Sederberg
  • Adam F. Osth
  • Trisha Van Zandt
  • Brandon M. Turner
Chapter
Part of the Computational Approaches to Cognition and Perception book series (CACP)

Abstract

In this chapter, we will present several algorithms, which differ in how they approximate the likelihood function and generate proposals for the posterior distribution, for performing likelihood-free inference. Four classes of algorithms—rejection-based, kernel-based, general methods, and hierarchical—will be discussed in great detail. We will provide a brief overview of the origins of each class as well as discussing the advantages and disadvantages of each. Finally, we will close the discussion by offering guidance on how to choose the appropriate class of algorithms for use in a given situation.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • James J. Palestro
    • 1
  • Per B. Sederberg
    • 1
  • Adam F. Osth
    • 2
  • Trisha Van Zandt
    • 1
  • Brandon M. Turner
    • 1
  1. 1.Department of PsychologyThe Ohio State UniversityColumbusUSA
  2. 2.University of MelbourneParkvilleAustralia

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