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A Tutorial on Joint Modeling

  • Brandon M. Turner
  • Birte U. Forstmann
  • Mark Steyvers
Chapter
Part of the Computational Approaches to Cognition and Perception book series (CACP)

Abstract

In this chapter, we provide a brief tutorial on joint modeling by using one simplified model as a running example. The chapter is split into three sections. In the first section, we describe the full generative model and explain how it can be used to produce hypothetical data by adjusting the model parameters. In the second section, we describe how to perform inference on data by estimating the model parameters. To facilitate the inference procedure, we provide code to run within the Just Another Gibbs Sampler (JAGS) [50] software. In the final section, we consider alternative parameterizations, covariates, and other models.

Keywords

Tutorial Bayesian estimation Generative model 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Brandon M. Turner
    • 1
  • Birte U. Forstmann
    • 2
  • Mark Steyvers
    • 3
  1. 1.Department of PsychologyThe Ohio State UniversityColumbusUSA
  2. 2.Cognitive Science CenterUniversity of AmsterdamAmsterdamThe Netherlands
  3. 3.Department of Cognitive SciencesUniversity of California, IrvineIrvineUSA

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