Bayesian inference for psychology  /

Introduction to Bayesian Inference for Psychology

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Description: We introduce the fundamental tenets of Bayesian inference, which derive from two basic laws of probability theory. We cover the interpretation of probabilities, discrete and continuous versions of Bayes' rule, parameter estimation, and model comparison. Using seven worked examples, we illustrate these principles and set up some of the technical background for the rest of this special issue of Psychonomic Bulletin & Review. Supplemental material is available via https://osf.io/wskex/.

License: CC-By Attribution 4.0 International

This project represents an accepted preprint submitted to PsyArXiv . Learn more about how to work with preprint files. View preprint

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Errata 1) The second equation in the right column on p15 has the same expression left and right on the first line. There is no reason for this. 2) The equation in the left column on p21 includes $I_\theta$ in the integral after the bounds had already been specified on the integral. The $I_\theta$ is superfluous and possibly confusing. Throughout the Measure of an Elf example, references to Runcor...

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    MATLAB/Octave and R code for Example 6

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    Introduction to Bayesian Inference for Psychology

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