A conceptual introduction to Bayesian data analysis, workflow, and a first model
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The three books cited in this lesson are all highly recommended readings. For beginners to Bayesian statistics, I recommend starting with Kruschke (2014), or McElreath (2020). Bayesian Data Analysis by Gelman et al. (2013) is the definitive textbook on the topic, and recommended for more mathematically oriented and/or advanced readers.
Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013. Bayesian Data Analysis, Third Edition. Boca Raton: Chapman; Hall/CRC.
Heino, Matti T. J., Matti Vuorre, and Nelli Hankonen. 2018. “Bayesian Evaluation of Behavior Change Interventions: A Brief Introduction and a Practical Example.” Health Psychology and Behavioral Medicine 6 (1): 49–78. https://doi.org/10.1080/21642850.2018.1428102.
Kruschke, John K. 2014. Doing Bayesian Data Analysis: A Tutorial Introduction with R. 2nd Edition. Burlington, MA: Academic Press.
McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. 2nd ed. CRC Texts in Statistical Science. Boca Raton: Taylor; Francis, CRC Press.
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Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/mvuorre/brms-workshop, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Vuorre (2020, Oct. 22). brms workshop: Introduction. Retrieved from https://mvuorre.github.io/brms-workshop/posts/introduction/
BibTeX citation
@misc{vuorre2020introduction, author = {Vuorre, Matti}, title = {brms workshop: Introduction}, url = {https://mvuorre.github.io/brms-workshop/posts/introduction/}, year = {2020} }