ID 2859211
Classroom 3 days
Webinar 5 days
Method Presentation with examples and hands-on labs.
Prequisite Basics in R and Statistics
Audience Data Analysts


  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training


Bayesian statistics is a subset of the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief or, more specifically, Bayesian probabilities. The general set of statistical techniques can be divided into a number of activities, many of which have special Bayesian versions. This training shows how to use Bayesian and probabilistic thinking to analyze data, to make predictions, and to fit models. In a first part, you will see the differences between the frequentist and probabilistic approach and see how you can use R for Bayesian statistics. In a second part, you will see how you can apply Bayesian inference as a method of statistical inference in which Bayes' rule is used to update the probability for a hypothesis as evidence is acquired. A third part focuses on the formulation of statistical models where the unique feature of Bayesian statistics consists in requiring the specification of prior distributions for any unknown parameters. The training closes with a part on machine learning / Data Mining for classification. The examples and hands-on labs are carried out using both R and OpenBUGS. OpenBUGS is a software for the Bayesian analysis of complex statistical models using Markov Chain Monte Carlo (MCMC) methods.

Training Dates

  • 2020-Nov-02 - Nov-06
  • 2021-Jan-11 - Jan-15
  • 2021-Mar-22 - Mar-26
  • 2021-May-31 - Jun-04

950 EUR +VAT

Location | Enrollment


Bayesian Statistics

Introduction: Quantifying Uncertainty Using Probabilities, Models and Prior Probabilities, Likelihoods and Posterior Probabilities, Bayesian Sequential Analysis - Review of Probability: Events and Sample Spaces, Unions - Intersections, Complements - Marginal and Conditional Probabilities - Bayes’ Rule - Addition and Multiplication Rules

One-Parameter Models

Bayesian Models - Prior Probability and Prior Distributions - The Posterior Distribution - Conjugate Priors - Inference for a Population Proportion: Frequentist Approach, Bayesian Inference, Bayesian Point Estimates - R for Bayesian Analysis - Inference Using Nonconjugate Priors on Mean and Variance - Noninformative Priors

Multiparameter Models

Informative Priors for Mean and Variance - Conjugate Joint Prior Density for Mean and Variance

Model Fit using Markov Chain Monte Carlo (MCMC)

Sampling-Based Methods - Markov Chain Monte Carlo (MCMC) Methods - Bayesian Models - Hierarchical Models: Fitting Bayesian Hierarchical Models, Estimation Based on Hierarchical Models - Software OpenBUGS

Regression and Hierarchical Regression Models

Review of Linear Regression - Introduction to Bayesian Simple Linear Regression - Generalized Linear Models - Hierarchical Normal Linear Models - Model Comparison, Model Checking, and Hypothesis Testing - Bayes Factors for Model Comparison and Hypothesis Testing - Bayes Factors and Bayesian Hypothesis Testing

Data Mining and Classification in Bayesian Statistics

Statistics for Machine Learning - Learning as Inference - Principal Components Analysis - Naive Bayes - Nearest Neighbour Classification - Gaussian Processes


Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

  • Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
  • System und Systematik von Fragebögen ISBN 978-3-939701-26-2
  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9

- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.


He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.