|
|
Programme
| Course 1: Bayesian Modelling and Computation |
|---|
| Programme on June 12, Monday. |
|---|
| 9AM--9:30AM | Welcome and Registration |
| 9:30AM--12:30PM | Morning Sessions |
|
Coffee Break 11--11:30AM |
1. Brief review of Bayesian principles; prior specifications; Bayesian inference and modelling.
2. Bayesian computation (Introduction): Importance sampling, Monte Carlo sampling and integration; Gibbs sampling and MCMC.
|
| 12:30PM-1:30PM | Lunch Break |
| 1:30PM -4:30PM | Afternoon Sessions |
| Tea Break: 3--3:30PM |
1. Bayesian computation(Advanced): MALA, Variational Bayes, ABC, Particle filters, Laplace approximation.
2. Hands on coding of the Gibbs sampler. Computer exercises with R.
|
| Course 1: Programme on June 13, Tuesday. |
|---|
| 9:30AM--12:30PM | Morning Sessions |
|
Coffee Break 11--11:30AM |
1. Bayes factor. Model comparison and selection. Information criteria.
2. Model adequacy and model averaging. Reversible Jump MCMC.
|
| 12:30PM-1:30PM | Lunch Break |
| 1:30PM -4:30PM | Afternoon Sessions |
| Tea Break: 3--3:30PM |
1. Introduction to WinBUGS with examples. Use of the R package CODA to analyse MCMC output.
2. Hands on session. Modelling tricks and tips for using WinBUGS.
|
| Course 1: Programme on June 14, Wednesday. |
|---|
| 9:30AM--12:30PM | Morning Sessions |
|
Coffee Break 11--11:30AM | 1. Bayesian hierarchical modelling,
Random effects models, Dynamic models, Latent variables, missing data.
2. Hands on session. Scripting in OpenBUGS and WinBUGS. Running winBUGS within R.
|
| 12:30PM-1:30PM | Lunch Break |
| 1:30PM -4:30PM | Afternoon Sessions |
| Tea Break: 3--3:30PM | 1. Discussion of other computing packages such as STAN and INLA.
2. One-on-one and group brainstorming sessions with the instructors where
participants can discuss modelling their own data sets.
|
| Participants can depart at 4:30PM. |
|---|
 |
|
| Course 2: Hierarchical modelling of spatial and
temporal data |
|---|
| Programme on June 15, Thursday. |
|---|
| 9--9:30AM | |
| 9:30AM--12:30PM | Morning Sessions |
|
Coffee Break 11--11:30AM | 1. Overview of spatial data; types of data, examples, projections; basics of areal data models, EDA; Markov random fields, CAR models.
2. Practical session I: Areal data modelling using WinBugs.
|
| 12:30PM-1:30PM | Lunch Break |
| 1:30PM -4:30PM | Afternoon Sessions |
| Tea Break: 3--3:30PM |
1. Basics of point referenced data models, spatial processes.
stationarity, variograms, spatial exploratory data analysis (EDA), kriging.
2. Practical session II: Variogram model fitting. Introduction to spBayes. Illustration of spatial modelling using R.
|
| Course 2: Programme on June 16, Friday. |
|---|
| 9:30AM--12:30PM | Morning Sessions |
|
Coffee Break 11--11:30AM |
1. Spatial misalignment; Model fitting for point pattern data.
2. Ecological data modelling. Practical illustration of analysing spatial point pattern data sets.
|
| 12:30PM-1:30PM | Lunch Break |
| 1:30PM -4:30PM | Afternoon Sessions |
| Tea Break: 3--3:30PM |
1. Spatio-temporal modeling; dimension reduction approaches for large datasets.
2. Hands on session on spatio-temporal modeling using spTimer.
Participants can discuss their own modelling problems with the instructors. |
| Participants can depart at 4:30PM. |
|---|
| |