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.
Isabel Cristina Maciel Natário
Weekly - 3
Total - 40
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.
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.
Two theoretical-practical assignments, needing R programming, one for each 3 curricular units (50% + 50%).
Module I – Introduction to Bayesian statistics
The bayesian paradigm
Prior and Posterior distributions
Module II – Spatial statistics
Programs where the course is taught: