Bayesian Data Analysis with Mathematica

Over the last ten years, Bayesian methods have grown from a specialist niche to become a mainstream development. Bayesian data analysis is a viable alternative to frequency-based, "by the book" statistics. It provides a principled approach to solving problems without "ad-hoceries". It can deal with every-day questions, as well as with problems where other methods are inconclusive or even fail. Therefore, Bayesian data analysis has become an important research tool in this era of Big Data.

for whom

This course aims at professionals, such as data scientists and Ph.D. students, working on real data problems. It provides novel insights as well as hands-on experience.

about the course

The course covers Bayes' theorem and the use of prior probabilities, the problem with maximum likelihood and over-fitting, and regression and model selection. This perennial problem of selecting a good model, while, simultaneously, finding a good fit to the data, is treated extensively. Time permitting, some examples of classification problems, kernel methods, or neural networks are shown.

Bayesian data analysis involves considerable non-linear optimization, for which the algorithms of Mathematica are well suited. A notebook with working examples is provided as course material. Emphasis is given to practical applicability and numerical efficiency.

The course is based on the excellent book: Pattern Recognition and Machine Learning by Christopher M. Bishop (Springer). Everyone is encouraged to have a look at this book.


A basic background in probability theory is necessary as well as proficiency in Mathematica at the level of the course Programming with Mathematica.


Regular courses are held at our office in Amsterdam. Sometimes we provide the courses on site. The location is then mentioned in the agenda below.


12 November 2015, 10.00-17.00 Amsterdam


Standard rate: euro 495+VAT
***Discounted rate: euro 95+VAT for (PhD) students


Dr. Romke Bontekoe

Dr. Romke Bontekoe got his PhD in astronomy at the University of Groningen on computer simulations of colliding galaxies. He worked in space research on the reconstruction of images from IRAS satellite data, already employing (pre-)Bayesian methods. As a long time independent consultant he has collaborated in many data analysis projects in business and in the life sciences. He wished that he had started with Mathematica earlier in his professional career.

All Mathematica 10 events:
3 November 2015 Programming with Mathematica (free) 10.00-17.00 Amsterdam subscribe
10 November 2015 Interactivity in Mathematica
an advanced course for constructing more fine tuned interactive output (free)
10.00-17.00 Amsterdam subscribe
12 November 2015 Bayesian Data Analysis with Mathematica 10.00-17.00 Amsterdam subscribe
18 November 2015 Image Processing and Analysis with Mathematica 10.00-17.00 Amsterdam subscribe
19 November 2015 Computer-Based Maths Summit 2 days London subscribe
24 November 2015 Introduction to Mathematica Part I (free) 10.00-17.00 Amsterdam subscribe
1 December 2015 Introduction to Mathematica Part II — includes pattern matching (free) 10.00-17.00 Amsterdam subscribe
8 December 2015 Programming with Mathematica (free) 10.00-17.00 Amsterdam subscribe
10 December 2015 Cloud computing with the Wolfram Language (free) 10.00-17.00 Amsterdam subscribe