Introduction te Bayesian Statistics

Central to Bayesian statistics is the idea that a ‘before’, a-priori, estimate of the probable value of a parameter is revised to an ‘after’, a posteriori, estimate based on new data. This idea fits in well with the way of thinking in medical decision-making. The Bayesian method offers the possibility to combine various data sources to update what is already known, while making inference about the uncertainty of the updated knowledge.

The participant is able to distinguish between different missing data mechanisms called missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR).

The course is intended for epidemiologists interested in Bayesian thinking, who want to become acquainted with performing simple Bayesian analyses.

Basic knowledge of probability theory, including: requencies,probabilities,bivariate probability distributions, means and expectations,variances; Statistical tests, confidence intervals; Regression models. Basic knowledge of R and the RStudio

We use an interactive Learn Management System for this online course. There you can find all course materials at least one week before the start of the course