ABS11 - 2011 Applied Bayesian Statistics School
HIERARCHICAL MODELING FOR ENVIRONMENTAL PROCESSES
EURAC, Bolzano/Bozen, Italy
June, 20-24, 2011
Monday 20/6
14.00-16.00 Lecture 1: Principles of Bayesian Inference, Hierarchical Modeling,
Bayesian Computation
16.00-16.30 Coffee break
16.30-17.30 Lecture 2: Introduction to spatial data and point-referenced data
17.30-19.00 Participants' talks:
- Marek Brabec
Uncertainty in PM10 spatial modelling
- Tuomas Kukko
Estimating the size and structure of local moose populations
- Claire McDonald
Plant species variability in the Allt a’Mharcaid Catchment, Scotland
- Consuelo Rubina Nava
Multinomial Mixed Logit Model: Bayesian Methods and Algorithms
- Claudia Notarnicola
Bayesian methodology for estimation of bio-geophysical parameters
from remotely sensed data
- Lucia Paci
A comparison between hierarchical spatio-temporal models in presence of
spatial homogeneous groups: the case of Ozone in Emilia-Romagna Region
20.00-22.00 Welcome buffet at EURAC
Tuesday 21/6
9.00-10.30 Lecture 2 (cont.): Point-referenced data, Areal unit data
10.30-11.00 Coffee break
11.00-12.30 Lecture 3: Hierarchical modeling for spatial data,
misalignment, disaggregation
12.30-14.00 Lunch at EURAC
14.00-16.00 Practical session 1: Bayesian computing – Winbugs, R, basic hierarchical models, first spatial analyses for areal data using Winbugs; examples – disease mapping, crime data (BCG book, Exercise 3.10)
16.00-16.30 Coffee break
16.30-18.00 Participants' talks:
- Paula Pereira
Characterizing Spatial-Temporal Forest Fires Patterns in Portugal
- Jacopo Soriano
Semiparametric Bayesian approaches to mixed effects models for outcome measures
in the treatment of acute myocardial infarction
- Rasoul Yousefpour
A Bayesian modelling and simulation concept for knowledge update in adaptive
management of forest resources under climate change
- Paolo Zanini
Models and data for the analysis of traffic flows
- Monika Zovko
Integrated environmental risks assessment in Croatian coastal river basins
Wednesday 22/6
9.00-11.00 Lecture 4: Multivariate spatial data and
space-time data
11.00-11.30 Coffee break
11.30-13.30 Practical session
2:
Analysis
of point-referenced spatial data, univariate case,
exploratory analysis using R, model fitting using sp-Bayes; examples –
temperature data, ozone data
Thursday 23/6
9.00-11.00 Lecture 5: Handling large spatial datasets, data fusion and downscaling
11.00-11.30 Coffee break
11.30-12.30 Lecture 6: Point
patterns
12.30-14.00 Lunch at EURAC
14.00-15.30 Lecture 7: Species
distributions – presence absence data and presence-only data
15.30-16.00 Coffee break
16.00-18.00 Practical session
3:
Bivariate
spatial data, space time data using a dynamic model; examples - forest
inventory data, daily temperature or ozone data. Point pattern data – perhaps not enough time
to do much, perhaps some simple analysis, G- functions, K-functions, kernel
intensity estimates; examples – locations of leukemia
cases, locations of tree species
20.00-22.00 Farewell dinner at a restaurant to be announced
Friday 24/6
9.00-10.30 Lecture 8: Analysis of
extremes in space-time data
10.30-11.00 Coffee break
11.00-13.00 Lecture 9: Wet sulfate and nitrate deposition. Distributed lags in space and time for ozone formation given temperature