Analysing Spatial Data in R: Worked examples: …
WinBUGS I BUGS stands for Bayesian inference Using Gibbs Sampler I Developed at the MRC and Imperial College London I Provides a generic language to Bayesian Hierarchical models I Models can be specified graphically as well I Several utilities to assess the convergence of the chain and display results I GeoBUGS is an extension …
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Analysing Spatial Data in R: Worked example: …
www.bias-project.org.ukWorked example: geostatistics I Geostatistics is a bit like the alchemy of spatial statistics, focussed more on prediction than model fitting I Since the reason for modelling is chiefly prediction in
Analysing Spatial Data in R: Worked examples: …
www.bias-project.org.ukAnalysing Spatial Data in R: Worked examples: disease mapping I Roger Bivand Department of Economics Norwegian School of Economics and Business Administration
Analysing Spatial Data in R: Worked example: …
www.bias-project.org.ukAnalysing Spatial Data in R: Worked example: point patterns Roger Bivand Department of Economics Norwegian School of Economics and Business Administration
Strategy for modelling non-random missing data mechanisms ...
www.bias-project.org.ukStrategy for modelling non-random missing data mechanisms in observational studies using Bayesian methods Alexina Mason1, Sylvia Richardson1, Ian Plewis2 and Nicky Best1 1Department of Epidemiology and Biostatistics, Imperial College London, UK 2Social Statistics, University of Manchester, UK Abstract Observational studies inevitably suffer from non-responses and missing values.