10 Matching Annotations
  1. Oct 2020
  2. seeing-theory.brown.edu seeing-theory.brown.edu
    Seeing Theory
    1
    1. ErikStuchly 27 Oct 2020

      Kunin, D. (n.d.). Seeing Theory. Retrieved October 27, 2020, from http://seeingtheory.io

      is:webpage lang:en COVID-19 statistics probability visualization tool interactive inference regression Bayesian
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    seeing-theory.brown.edu/
  3. Aug 2020
  4. psyarxiv.com psyarxiv.com
    Measuring evidence for mediation in the presence of measurement error
    1
    1. ErikStuchly 18 Aug 2020

      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

      is:preprint lang:en mediation measurement error analysis experimental manipulation scientific method methodology statistics indeterminacy modeling Bayesian framework
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    psyarxiv.com/5bz3f/
  5. Jul 2020
  6. jasp-stats.org jasp-stats.org
    Introducing JASP 0.13 - JASP - Free and User-Friendly Statistical Software
    1
    1. ErikStuchly 04 Jul 2020

      Introducing JASP 0.13. (2020, July 2). JASP - Free and User-Friendly Statistical Software. https://jasp-stats.org/?p=6483

      is:webpage lang:en JASP statistics software modeling Bayesian data analysis update R module improvement bug fix is:blog
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  7. Jun 2020
  8. psyarxiv.com psyarxiv.com
    JASP (Software)
    1
    1. Marlene_Wulf 04 Jun 2020

      Han, H., & Dawson, K. J. (2020). JASP (Software) [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/67dcb

      is:preprint lang:en Bayesian analysis quantitative psychology Bayesian statistics statistical analysis quantitative method JSAP open-source free statistical software package statistical testing psychological science APA
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    psyarxiv.com/67dcb/
  9. May 2020
  10. psyarxiv.com psyarxiv.com
    Analyzing nonresponse in longitudinal surveys using Bayesian additive regression trees: A nonparametric event history analysis
    1
    1. edampf 07 May 2020

      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

      is:preprint lang:en Bayesian panel study nonresponse regression tree Bayes longitudinal BART statistics survey dropout modeling
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    psyarxiv.com/82c3w/
  11. github.com github.com
    rmcelreath/statrethinking_winter2019
    1
    1. edampf 07 May 2020

      McElreath, R. Statistical Rethinking: A Bayesian Course Using R and Stan Github.com. https://github.com/rmcelreath/statrethinking_winter2019

      Entire course with materials online.

      is:other lang:en Bayesian Stan R statistics MPI code Github lecture course
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    github.com/rmcelreath/statrethinking_winter2019
  12. Apr 2020
  13. psyarxiv.com psyarxiv.com
    Priors in a Bayesian Audit: How Integration of Existing Information into the Prior Distribution Can Increase Transparency, Efficiency, and Quality
    1
    1. Marlene_Wulf 28 Apr 2020

      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

      is:preprint lang:en Bayesian statistics audit financial statement prior distribution testing phase analysis
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    psyarxiv.com/8fhkp/
  14. Jan 2016
  15. blogs.scientificamerican.com blogs.scientificamerican.com
    Bayes's Theorem: What's the Big Deal?
    3
    1. mshook 28 Jan 2016
      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.
      bayes bayesian math stats statistics january 2016 critique probability
    2. mshook 28 Jan 2016
      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?
      bayes bayesian stats statistics belief math kalman critique january 2016
    3. mshook 28 Jan 2016
      Initial belief plus new evidence = new and improved belief.
      bayes bayesian stats statistics critique kalman january 2016
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    blogs.scientificamerican.com/cross-check/bayes-s-theorem-what-s-the-big-deal/