Bayesian inference

THE FUTURE OF STATISTICS

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.

Contact us about closed training
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