Villa del Grumello, Como, Italy
19-23 June 2017

ABS Schools

The Applied Bayesian Statistics summer school has been running since 2004. From 2012 it is organized by

Since 2014 the school is organized in cooperation with Fondazione "Alessandro Volta"

The school aims to present state-of-the-art Bayesian applications, inviting leading experts in their field.
Each year a different topic is chosen.

Past editions were devoted to: Gene Expression Genomics, Decision Modelling in Health Care, Spatial Data in Environmental and Health Sciences, Bayesian Methods and Econometrics, Bayesian Decision Problems in Biostatistics and Clinical Trials, Bayesian Methodology for Clustering, Classification and Categorical Data Analysis, Bayesian Machine Learning with Biomedical Applications, Hierarchical Modeling for Environmental Processes, Stochastic Modelling for Systems Biology, Bayesian Methods for Variable Selection with Applications to High-Dimensional Data and Applied Bayesian Nonparametrics, Modern Bayesian Methods and Computing for the Social Sciences, Bayes, Big Data, and the Internet.

Topic and Lecturer

Course Outline

This course is intended for students that have a background in statistical methods and modeling. The course is focused on models for data that are spatially referenced and that evolve in time. We will develop models for stochastic processes that are indexed at irregularly scattered, fixed, locations. We will look into the theoretical properties of those models as well as into the computational issues involved in the estimation of their parameters. We will extend the analysis of fields of spatial observations that are collected in time. In particular, we will consider dynamically varying process where space and time interact. Real-data applications of Bayesian methods with MCMC techniques will be illustrated.

  • Day 1: Introduction to Bayesian methods and hierarchical models. Examples of spatially referenced data. Basic properties of Gaussian random fields. Graphical exploration of spatial fields.
  • Day 2: Variograms. Examples of families of correlations functions. Bayesian approach to estimation and prediction of spatial random fields.
  • Day 3: The big data problem: reduced rank models and other modern approaches to dimension reduction.
  • Day 4: Spatio-temporal models. Dynamic linear models: integro-differential equations.
  • Day 5: Extensions


Have a look at 2017 course detailed programme


We will use the following R packages (available from CRAN):

  1. geoR
  2. fields
  3. spBayes


  1. Hierarchical Modeling and Analysis for Spatial Data, Second Edition, S. Banerjee, B.P. Carlin and A.E. Gelfand. Chapman and Hall.
  2. Statistics for Spatial-Temporal Data, N.A.C. Cressie and C.K. Wikle. Wiley.
  3. Model-based Geostatistics, P.J. Diggle and P.J. Ribeiro. Springer
  4. Handbook of Spatial Statistics, A.E. Gelfand, P.J. Diggle, M. Fuentes and P. Guttorp (eds). CRC Press.

Location and Schedule

The 2017 school will be held at Villa del Grumello, a magnificent villa located in the city of Como, along the Lake Como shoreline.

Please note that the number of available places is limited.

School timetable:
start time - Monday, June, 19th, at 2 p.m.
end time - Friday, June, 23rd at 1 p.m..

Registration & Accommodation

Since a limited number of places is available, we strongly encourage participants to register as soon as possible. Please note that the registration form can be filled only if you are able to provide some data which are necessary according to the current Italian laws

Registration Accommodation

Participants & Past Editions

Check list of participants or have a look at previous school editions

Participant list Past editions