The Bayesian Modelling Summer School (bookings closed)

Description:

Course Overview:

This training course will introduce powerful Bayesian spatial and spatial-temporal modelling techniques that enable researchers to analyse small area spatial-temporal data arising in the social, economic, political and public health sciences. The course will provide not only the underlying statistical theory for data analysis but also the hands-on experience necessary to apply different spatial and spatial-temporal techniques to visualise and analyse a wide variety of practical datasets.

Course Outline:

• Types and properties of spatial and spatial-temporal data and implications for model building;
• Techniques for testing spatial heterogeneity and autocorrelation; clusters and hotspot detection;
• Uses of maps and graphics for visualization using R;
• Bayesian inference for spatial and spatial-temporal data;
• Introduction to Markov chain Monte Carlo methods;
• Simple Bayesian regression models;
• Bayesian hierarchical models for spatial data, both continuous (e.g., normal/log normal data) and discrete (e.g., count/binary data);
• Implementation of various Bayesian hierarchical models in WinBUGS for spatial data;
• Introduction to spatial econometric models;
• Regression diagnostics with particular relevance for spatial data;
• Introduction to modelling small area time series data, including linear models in time, autoregressive models and interrupted time series models;
• Introduction to various strategies/structures for modelling spatial-temporal data;
• Discussion of space-time models using practical examples.

Key Learning Outcomes:

You will gain a broad knowledge of the diversity of current approaches to modelling small area spatial and spatial-temporal data. Through practical sessions, you will acquire hands-on experience in analysing spatial and spatial-temporal data arising in different fields. Upon completion of the course, you should be able to:

1. use relevant knowledge to analyse small area spatial and spatial-temporal datasets, from exploratory analysis to model development, and to produce relevant model outputs for answering substantive questions.
2. manage, manipulate and visualise spatial and spatial-temporal data using R and implement Bayesian spatial and spatial-temporal models via WinBUGS.
3. recognise issues and challenges presented in a spatial/spatial-temporal dataset with the aim to inform future model development, via modifying existing methodology and/or developing new modelling techniques.

Application Process

NCRM are providing this training free of charge to a maximum of 20 researchers working in UK based organisations. Applications are made via Eventbrite (link is below) and will close at 4pm on 31 March 2023. We will aim to inform all applicants of the outcome by 24 April 2023. This is to allow those that are successful time to clear any schedules and participate in the pre-sessional discussions and preparation.

Criteria:

• You must be living in the UK with no visa restrictions
• Priority will be given to Early Career Researchers from any discipline or place of employment with a limited number of places for final year PhD students.

Successful applicants will receive assigned hotel accommodation (if required), standard class public transport reimbursement (paid in arrears), daytime catering and one group evening meal.

Cost:

Applications only

Region:

North West

Keywords:

Multilevel Modelling , Spatial Data Analysis, Quantitative Software

Related publications and presentations:

Multilevel Modelling
Spatial Data Analysis
Quantitative Software

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