Biomedical Statistics


Students must be familiarized with concepts of bayesian statistical inference, very much relevant in important modern statistical analyses in life sciences and health, and with statistical methods where the spatial component of what is being studied is relevant and cannot be ignored, so they can employ them properly in the resolution of real life problems, to analyse data, estimate and predict the corresponding underlying phenomena. 

General characterization





Responsible teacher

Isabel Cristina Maciel Natário


Weekly - 3

Total - 40

Teaching language



Basic notions of Analysis and intermediate level notions of Probability and Statistics.


Albert J. (2007). Bayesian Computation with R. Springer.

Lee P. (1998). Bayesian Statistics: an introduction. Halsted Press.

Seefeld K & Linder E. (2007). Statistics Using R with Biological
Examples. Department of Mathematics and Statistics, University of
New Hampshire, Durham, New Hampshire USA.

Turkman MAA & Paulino CD. (2015). Estatística Bayesiana Computacional: uma Introdução. Sociedade Portuguesa de Estatística.

Bivand RS & Pebesma EJ & Gómez-Rubio V (2011). Applied Spatial Data Analysis with R. Springer.

Carvalho ML & Natário I (2008). Análise de Dados Espaciais. Sociedade Portuguesa de Estatística.

Gelman A & Carlin JB \& Stern HS and Rubin DB (2004). Bayesian Data Analysis. Chapman & Hall/CRC.

Waller LA & Gotway CA (2004). Applied Spatial Statistics for Public Health Data. Wiley.

Teaching method

The curricular unit is divided into 6 learning units that include each 2-3 videos of oral exposition of the contents, of 10-15 minutes each, along with the presentation of examples and complemented by solved proposed exercises. In the end of each learning unit a revising exercise is delivered. During the learning units presentation two evaluation assignments are distributed, contributing for the final grading. A timetable for answering questions to students made is available in person or via Skype.

Evaluation method

Two theoretical-practical assignments, needing R programming, one for each 3 curricular units (50% + 50%).

Subject matter

Module I – Introduction to Bayesian statistics
The bayesian paradigm
Prior and Posterior distributions
Bayesian inference

Module II – Spatial statistics
Areal data
Geostatistical data
Point patterns


Programs where the course is taught: