Probabilistic programming with (R)Stan

Probabilistic models describe how the observed data was generated, and what structure the signal and noise from potentially multiple sources may have. Many classical statistical models are special cases of probabilistic models with special modeling assumptions. Probabilistic models can be implemented, improved, and critizised in a flexible, explicit and transparent manner, and the analysis can be supported with prior information about the data.
This 1-day course provides an introduction to Bayesian/probabilistic models. We will implement standard linear models based on the rstanarm package of the R statistical programming environment and readily available example data sets. The workshop is an ideal opportunity to familiarize yourself with the basic ideas in probabilistic modeling such as prior information, likelihood, model criticism and validation, as well as some of the available tools. At the end, you should be able to implement basic probabilistic models yourself, and understand their relative advantages and pitfalls compared to their classical alternatives.

Scientific topics: Statistics and probability

Target audience: Life Science Researchers, PhD students, beginner bioinformaticians, post-docs

Authors: Leo Lahti

Remote created date: 2016-04-22

Probabilistic programming with (R)Stan https://tess.elixir-europe.org/materials/probabilistic-programming-with-r-stan Probabilistic models describe how the observed data was generated, and what structure the signal and noise from potentially multiple sources may have. Many classical statistical models are special cases of probabilistic models with special modeling assumptions. Probabilistic models can be implemented, improved, and critizised in a flexible, explicit and transparent manner, and the analysis can be supported with prior information about the data. This 1-day course provides an introduction to Bayesian/probabilistic models. We will implement standard linear models based on the rstanarm package of the R statistical programming environment and readily available example data sets. The workshop is an ideal opportunity to familiarize yourself with the basic ideas in probabilistic modeling such as prior information, likelihood, model criticism and validation, as well as some of the available tools. At the end, you should be able to implement basic probabilistic models yourself, and understand their relative advantages and pitfalls compared to their classical alternatives. Statistics and probability Life Science Researchers PhD students beginner bioinformaticians post-docs 2016-04-22