ABS17@IMATI-CNR

Details

Monday June 19

13.00
Registration
14.00 - 16.00
Introduction to spatial statistics. Discussion of different types of data and problems. Discussion of references and software. Examples of spatially referenced data. Graphical exploration of spatial fields.
16.00 - 16.30
Coffee break
16.30 - 18.00
Introduction to Bayesian methods and hierarchical models

Tuesday June 20

09.00 - 10.30
Basic properties of Gaussian random fields. Smoothness. Spectral densities. Examples of families of correlations functions. Traditional approaches to estimation: variograms
10.30 - 11.00
Coffee break
11.00 - 12.30
Maximum likelihood estimation. Prediction for spatial random fields: kriging.
12.30 - 14.00
Lunch at Villa del Grumello
14.00 - 16.00
Practical session

Installing and running R software for exploring and fitting spatial data with geoR and spBayes

16.00 - 16.30
Coffee break
16.30 - 19.10
Participants' talks Download details

Air Quality @ ARPAE Emilia-Romagna Roberta Amorati e Chiara Agostini

Feature selection for spatial point processes intensity estimation via regularization method Achmad Choiruddin

Passenger flows through airport terminals Anthony Ebert

(To Be Announced) Francesco Gabriele

Bayesian hierarchical modelling to estimate the risk of psychiatric disease in teachers Maria Lodolo D'Oria

Formal language and spatio-temporal analysis for ecological systems Ludovica Luisa Vissat

(Bayesian) Circular Statistics Kees Mulder

Investigation of risk factors of chronic airflow obstruction (CAO) in the BOLD study using Bayesian methods Jaymini Patel

Tracking the Evolution of Rainfall Precipitation Fields Using Persistent Maxima Simone Pittaluga

Future interests: environmental adaptive sampling Luigi Rocca

Fast Bayesian spatial 3D priors for brain imaging Per Siden

The Analysis of Economic Time Series with R Giuseppe Smigliani

(To Be Announced) Christien Thiart

Wednesday June 21

09.00 - 10.30
Bayesian approach to estimation. Bayesian kriging.
10.30 - 11.00
Coffee break
11.00 - 13.00
The big data problem: reduced rank models and other modern approaches to dimension reduction. Process convolutions, predictive Gaussian processes
13.00
Free afternoon and evening

Thursday June 22

09.00 - 10.30
Practical session

Fitting predictive Gaussian processes with spBayes

10.30 - 11.00
Coffee break
11.00 - 12.30
Multivariate spatial models. Cross correlation functions. Coregionalization.
12.30 - 14.00
Lunch at Villa del Grumello
14.00 - 15.30
Spatio-temporal models. Space-time covariance functions. Quick introduction to dynamic linear models. Conditional linear models for space-times data
15.30 - 16.00
Coffee break
16.00 - 18.00
Practical session

Fitting multivariate spatial models with spBayes

19.30
Farewell dinner

Friday June 23

09.00 - 11.00
Integro-differential equations (IDEs). Dynamic linear models to fit IDEs
11.00 - 11.30
Coffee break
11.30 - 13.00
Practical session

Fitting space-time data using IDEs