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.
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Turner, B.M., Forstmann, B.U., Steyvers, M. (2019). A Tutorial on Joint Modeling. In: Joint Models of Neural and Behavioral Data. Computational Approaches to Cognition and Perception. Springer, Cham. https://doi.org/10.1007/978-3-030-03688-1_2
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DOI: https://doi.org/10.1007/978-3-030-03688-1_2
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