Bayesian inference


Bayesian inference

Training description

The training covers a broad range of topics on Bayesian inference and its implementation in R.

Duration: 2 days 8 hours each (including an hour lunch break)

Requirements: basic knowledge of statistics.

Training agenda

Part one: The foundation

  • Crisis in statistics – why Bayesian approach is the future?
  • Why p-value still rules at universities?
  • Issues and tools of the classic approach
  • The alternative of Bayesian approach and its workflow

Part two: The theory of everything

  • Prior function, probability function and posterior function
  • Selecting the optimal prior function
  • Conjunction in Bayesian approach
  • Verification of hypotheses in Bayesian approach – Bayes factor
  • M type error and S type error
  • Estimating future values using posteriori function

Part three: Let’s code!

  • Probabilistic programming languages (PPL) – the story
  • Overview of languages PPL – BUGS, Jags and Stan
  • MCMC – Bayesian sampling approach

Part four: Checking won’t hurt

  • Diagnosing of Bayesian models
  • Detection of MCMC sampling abnormalities

Part five: Specialised-haul

  • Overview of R/Python packages supporting modelling in Bayesian approach

Part six: The small ones matter, too

  • Shrinkage – the concept of shrinking
  • Hierarchical modelling in Bayesian approach

Upcoming open trainings

Currently, no open training covering the given issue is planned. We encourage you to contact us regarding the closed training.

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