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https://github.com/avehtari/BDA_R_demos

Bayesian Data Analysis demos for R
https://github.com/avehtari/BDA_R_demos

bayesian bayesian-data-analysis bayesian-inference mcmc monte-carlo r stan

Last synced: 4 months ago
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Bayesian Data Analysis demos for R

Host: GitHub
URL: https://github.com/avehtari/BDA_R_demos
Owner: avehtari
License: bsd-3-clause
Created: 2016-02-10T13:28:21.000Z (over 8 years ago)
Default Branch: master
Last Pushed: 2024-02-22T16:34:28.000Z (5 months ago)
Last Synced: 2024-02-22T17:55:34.977Z (5 months ago)
Topics: bayesian, bayesian-data-analysis, bayesian-inference, mcmc, monte-carlo, r, stan
Language: HTML
Homepage: https://avehtari.github.io/BDA_course_Aalto/demos.html
Size: 165 MB
Stars: 546
Watchers: 38
Forks: 203
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE.txt

Lists

awesome-stars - avehtari/BDA_R_demos - Bayesian Data Analysis demos for R (HTML)
awesome-stars - BDA_R_demos - Bayesian Data Analysis demos for R (HTML)

README

        # Bayesian Data Analysis R Demos

The [BDA_R_demos repository](https://github.com/avehtari/BDA_R_demos) contains some R demos and additional notes for the book [Bayesian Data

Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3)](http://www.stat.columbia.edu/~gelman/book/). See also [Bayesian Data Analysis course material](https://github.com/avehtari/BDA_course_Aalto).

Currently there are demos for BDA3 Chapters 2, 3, 4, 5, 6, 10, 11 and 12.

Furthermore there are demos for

[CmdStanR](https://github.com/stan-dev/cmdstanr),

[RStan](https://github.com/stan-dev/rstan)

[RStanARM](https://github.com/stan-dev/rstanarm).

The initial demos were originally written for Matlab by [Aki

Vehtari](http://users.aalto.fi/~ave/) and translated to R by [Markus

Paasiniemi](https://github.com/paasim). Recently more demos have been

added for [CmdStanR, RStan and RStanARM](demos_rstan).

Unless otherwise specified in specific files all code licensed

under BSD-3 and all text, slides and figures licensed under CC-BY-NC 4.0.

The corresponding [Python demos](https://github.com/avehtari/BDA_py_demos)

and the corresponding [Matlab/Octave demos](https://github.com/avehtari/BDA_m_demos).

See also [Model Selection tutorial](https://avehtari.github.io/modelselection/).

List of demos (not including [rstan and rstanarm demos](demos_rstan))

- [Chapter 2](demos_ch2)

  - [demo2_1: Probability of a girl birth given placenta previa (BDA3 p. 37)](https://avehtari.github.io/BDA_R_demos/demos_ch2/demo2_1.html)

  - [demo2_2: Illustrate the effect of prior in binomial model](https://avehtari.github.io/BDA_R_demos/demos_ch2/demo2_2.html)

  - [demo2_3: Illustrate simulation based inference](https://avehtari.github.io/BDA_R_demos/demos_ch2/demo2_3.html)

  - [demo2_4: Illustrate grid and inverse-cdf sampling](https://avehtari.github.io/BDA_R_demos/demos_ch2/demo2_4.html)

- [Chapter 3](https://avehtari.github.io/BDA_R_demos/demos_ch3)

  - [demo3_1_4: Normal model with unknown mean and variance (BDA3 section 3.2 on p. 64)](https://avehtari.github.io/BDA_R_demos/demos_ch3/demo3_1_4.html)

  - [demo3_5: Estimating the speed of light using normal model BDA3 p. 66](https://avehtari.github.io/BDA_R_demos/demos_ch3/demo3_5.html)

  - [demo3_6: Binomial regression and grid sampling with bioassay data (BDA3 p. 74-)](https://avehtari.github.io/BDA_R_demos/demos_ch3/demo3_6.html)

- [Chapter 4](https://avehtari.github.io/BDA_R_demos/demos_ch4)

  - [demo4_1: Normal approximation for binomial regression model and Bioassay data](https://avehtari.github.io/BDA_R_demos/demos_ch4/demo4_1.html)

- [Chapter 5](https://avehtari.github.io/BDA_R_demos/demos_ch5)

  - [demo5_1: Hierarchical model for Rats experiment (BDA3, p. 102)](https://avehtari.github.io/BDA_R_demos/demos_ch5/demo5_1.html)

  - [demo5_2: Hierarchical model for SAT-example data (BDA3, p. 102)](https://avehtari.github.io/BDA_R_demos/demos_ch5/demo5_2.html)

- [Chapter 6](https://avehtari.github.io/BDA_R_demos/demos_ch6)

  - [demo6_1: Posterior predictive checking of normal model for light data](https://avehtari.github.io/BDA_R_demos/demos_ch6/demo6_1.html)

  - [demo6_2: Posterior predictive checking for independence in binomial trials](https://avehtari.github.io/BDA_R_demos/demos_ch6/demo6_2.html)

  - [demo6_3: Posterior predictive checking of normal model with poor test statistic](https://avehtari.github.io/BDA_R_demos/demos_ch6/demo6_3.html)

  - [demo6_4: Marginal posterior predictive checking with PIT test](https://avehtari.github.io/BDA_R_demos/demos_ch6/demo6_4.html)

- Chapter 7

  - See [model selection tutorial](https://github.com/avehtari/modelselection_tutorial)

- [Chapter 10](https://avehtari.github.io/BDA_R_demos/demos_ch10)

  - [demo10_1: Rejection sampling](https://avehtari.github.io/BDA_R_demos/demos_ch10/demo10_1.html)

  - [demo10_2: Importance sampling](https://avehtari.github.io/BDA_R_demos/demos_ch10/demo10_2.html)

  - [demo10_3: Importance sampling with normal distribution as a proposal for Bioassay model](https://avehtari.github.io/BDA_R_demos/demos_ch10/demo10_3.html)

- [Chapter 11](https://avehtari.github.io/BDA_R_demos/demos_ch11)

  - [demo11_1: Gibbs sampling illustration](https://avehtari.github.io/BDA_R_demos/demos_ch11/demo11_1.html)

  - [demo11_2: Metropolis sampling + convergence illustration](https://avehtari.github.io/BDA_R_demos/demos_ch11/demo11_2.html)

  - [demo11_3_4: Metropolis sampling + convergence illustration](https://avehtari.github.io/BDA_R_demos/demos_ch11/demo11_3_4.html)

  - [demo11_5: Diagnostics with posterior and bayesplot packages](https://avehtari.github.io/BDA_R_demos/demos_ch11/demo11_5.html)

- [Chapter 12](https://avehtari.github.io/BDA_R_demos/demos_ch12)

  - [demo12_1: Static Hamiltonian Monte Carlo illustration](https://avehtari.github.io/BDA_R_demos/demos_ch12/demo12_1.html)

  - [demo12_2: NUTS / Dynamic Hamiltonian Monte Carlo illustration](https://avehtari.github.io/BDA_R_demos/demos_ch12/demo12_2.html)

- [CmdStanR, RStan, RStanARM](https://avehtari.github.io/BDA_R_demos/demos_rstan)