- Apr 2022
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twitter.com twitter.com
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ReconfigBehSci on Twitter: ‘RT @JASPStats: How to obtain introductory texts using the Learn Bayes Module in JASP. #stats #openSource https://t.co/dn7jyFr59i’ / Twitter. (n.d.). Retrieved 6 March 2021, from https://twitter.com/SciBeh/status/1326512599903694848
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- Jul 2021
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Romero, P., Mikiya, Y., Nakatsuma, T., Fitz, S., & Koch, T. (2021). Modelling Personality Change During Extreme Exogenous Conditions. PsyArXiv. https://doi.org/10.31234/osf.io/rtmjw
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- May 2021
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www.learnbayesstats.com www.learnbayesstats.com
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The ‘Learning Bayesian Statistics’ podcast. (n.d.). Retrieved 13 May 2021, from https://www.learnbayesstats.com
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- Mar 2021
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twitter.com twitter.com
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ReconfigBehSci. (2020, October 27). RT @JASPStats: How to perform Robust Bayesian Meta-Analysis in JASP. To learn more, have a look at the tutorial video: Https://t.co/4fmkLEH… [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1321387314887708672
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- Oct 2020
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seeing-theory.brown.edu seeing-theory.brown.edu
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Kunin, D. (n.d.). Seeing Theory. Retrieved October 27, 2020, from http://seeingtheory.io
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- Aug 2020
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Laghaie, A., & Otter, T. (2020). Measuring evidence for mediation in the presence of measurement error [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/5bz3f
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- Jul 2020
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jasp-stats.org jasp-stats.org
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Introducing JASP 0.13. (2020, July 2). JASP - Free and User-Friendly Statistical Software. https://jasp-stats.org/?p=6483
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- Jun 2020
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psyarxiv.com psyarxiv.com
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Han, H., & Dawson, K. J. (2020). JASP (Software) [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/67dcb
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- May 2020
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psyarxiv.com psyarxiv.com
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Zinn, S., & Gnambs, T. (2020, April 18). Analyzing nonresponse in longitudinal surveys using Bayesian additive regression trees: A nonparametric event history analysis. https://doi.org/10.31234/osf.io/82c3w
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github.com github.com
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McElreath, R. Statistical Rethinking: A Bayesian Course Using R and Stan Github.com. https://github.com/rmcelreath/statrethinking_winter2019
Entire course with materials online.
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- Apr 2020
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psyarxiv.com psyarxiv.com
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Derks, K., de swart, j., van Batenburg, P., Wagenmakers, E., & wetzels, r. (2020, April 28). Priors in a Bayesian Audit: How Integration of Existing Information into the Prior Distribution Can Increase Transparency, Efficiency, and Quality. Retrieved from psyarxiv.com/8fhkp
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- Jan 2016
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blogs.scientificamerican.com blogs.scientificamerican.com
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P(B|E) = P(B) X P(E|B) / P(E), with P standing for probability, B for belief and E for evidence. P(B) is the probability that B is true, and P(E) is the probability that E is true. P(B|E) means the probability of B if E is true, and P(E|B) is the probability of E if B is true.
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The probability that a belief is true given new evidence equals the probability that the belief is true regardless of that evidence times the probability that the evidence is true given that the belief is true divided by the probability that the evidence is true regardless of whether the belief is true. Got that?
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Initial belief plus new evidence = new and improved belief.
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