Prof Sujit K Sahu

          

   

Two short-courses on Bayesian modelling and hierarchical modelling of spatial and temporal data.

Professor Alan Gelfand and Prof Sujit Sahu

June 1-4, 2015

See below for a tentative programme and registration information.

Course 1: Bayesian Modelling and Computation, June 1-2, 2015.

The first short-course on "Bayesian Modelling and Computation" is aimed at applied scientists who are thinking of using Bayesian methods and would like to receive a gentle introduction with a large practical component.

No previous knowledge of Bayesian methods is necessary. However, some familiarity with standard probability distributions (normal, binomial, Poisson, gamma) and standard statistical methods such as multiple regression will be assumed.

Theory lectures on the Bayes theorem, elements of Bayesian inference, choice of prior distributions and introduction to MCMC will be followed by hands-on experience using R and the WinBUGS software. Some of the data analysis examples discussed here will be enhanced by using spatial statistics methods in the second course.

More advanced methods using Hamiltonian MCMC, reversible jump, INLA, Variational Bayes, and ABC will also be introduced.

Course 2: Hierarchical modelling of spatial and temporal data, June 3-4, 2015

This course will provide an overview of current ideas in statistical inference methods appropriate for analysing various types of spatially point referenced data, some of which may also vary temporally.

The course begins with an outline of the three types of spatial data: point-level (geostatistical), areal (lattice) and spatial point process, illustrated with examples from environmental pollution monitoring and epidemiological disease mapping.

Exploratory data analysis tools and traditional geostatistical modelling approaches (variogram fitting, kriging, and so forth) are described for point referenced data, along with similar presentations for areal data models. These start with choropleth maps and other displays and progress towards more formal statistical concepts, such as the conditional, intrinsic, and simultaneous autoregressive (CAR, IAR, and SAR) models so often used in conjunction with spatial disease mapping.

The heart of the course will cover hierarchical modelling for spatial response data, including Bayesian kriging and lattice modelling. More advanced issues will also be covered, such as nonstationarity (mean level depending on location) and anisotropy (spatial correlation depending on direction as well as distance). Bayesian methods will also be discussed for modelling data that are spatially misaligned (say, with one variable measured by post-code and another by census tract), since they are particularly well-suited to sorting out complex interrelationships and constraints.

The course concludes with a brief discussion of spatio-temporal and spatial survival models, both illustrated in the context of cancer control and epidemiology. Computer implementations for the models via the WinBUGS and R packages will be described throughout.

Participants are encouraged to buy the book Hierarchical Modeling and Analysis for Spatial Data, co-authored by Professor Gelfand.

These courses are likely to be very popular, as when these were run biennially since 2005.

Programme

Programme

Course 1: Bayesian Modelling and Computation: Programme on June 1, Monday.
9:0AM--9:30AM Registration
Morning Session 9:30AM -12:30PM, Coffee Break 11--11:30AM Brief review of Bayesian principles; prior specifications; hierarchical modeling, random effects, missing data, latent variables.
Lunch Break 12:30PM-1:30PM
Afternoon Session 1:30PM -4:30PM,
Tea Break: 3--3:30PM
Bayesian computation (Introduction) -- Importance sampling, Monte Carlo sampling and integration; Gibbs sampling and MCMC (theory and implementation); examples; computer exercises with R and winBugs
Course 1: Programme on June 2, Tuesday.
Morning Session 9:0AM--12:30PM Coffee Break 10:30--11AM Model adequacy, model selection, model averaging; multivariate models; dynamic models, particle filters and ABC.
Lunch Break 12:30PM-1:30PM
Afternoon Session 1:30PM -4:30PM
Tea Break: 3--3:30PM
Bayesian computation(Advanced) -- Reversible Jump MCMC; MALA, Variational Bayes, Laplace approx. and INLA.
Course 2: Hierarchical modelling of spatial and temporal data Programme on June 3, Wednesday.
9:0AM--9:30AM Registration
Morning Session 9:30AM -1:30PM Coffee Break 11--11:30AM Overview of spatial data; types of data, examples, projections; basics of areal data models, EDA; Markov random fields, CAR models.
Practical session I: Areal data modelling using WinBugs.
Basics of point referenced data models, spatial processes. stationarity, variograms, spatial exploratory data analysis (EDA), kriging,
Lunch Break 1:30PM-2:30PM
Afternoon Session 2:30PM -4:30PM Practical session II: Variogram model fitting.
Practical Session III: Introduction to spBayes. Illustration of spatial modelling using R.
Course 2: Programme on June 4, Thursday.
Morning Session 9:0AM--12:30PM Coffee Break 10:30--11AM Spatial misalignment; Model fitting for point pattern data; spatio-temporal modeling; spatial point patterns; dimension reduction approaches for large datasets.
Lunch Break 12:30PM-1:30PM
Afternoon Session 1:30PM -4:30PM
Tea Break: 3--3:30PM
Practical Session IV: Illustration of spatial point patterns.
Practical Session V: Illustration of Spatio-temporal modeling using spTimer

Registration Information

Course 1: Bayesian Modelling and Computation, June 1-2, 2015.

Research students £200
Academics £250
All others £300

Course 2: Hierarchical modelling of spatial and temporal data, June 3-4, 2015

Research students £200
Academics £250
All others £300
  • The fee will include course materials, computing facilities, lunch and refreshments each day.
  • University of Southampton staff and students will receive a 20% discount on the above prices. Please email the professional training secretary if you require any assistance.
Payments can be made by the University online store:
  • The number of spaces is limited, so an early registration is advised.
  • Fees can be refunded in full if cancelled before April 30, 2015.
  • Participants are required to book their own accommodation.