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BayesianExperiments.jl provides a toolbox for running various types of Bayesian AB testing experiments.

Current features include:

  • Hypothesis testing with Bayes factor. Support the effect size model with Normal distribution prior and JZS prior.
  • Bayesian decision making with conjugate prior models. Support expected loss and probability to beat all as the stopping rule.
  • Flexible experiment design for both fixed horizon experiments and sequential test experiment.
  • Efficient simulation tools to support power analysis and sensitivity analysis.

Documentation and Examples

For usage instructions and tutorials, see documentation.

For detailed discussions on many topics in the field, see the Jupyter notebooks in the examples folder:

Or you can go to binder to directly play with the Jupyter notebooks.

Open source projects in R related to our project: