2026 Regression Models Using Bayesian Estimation-申请方
主办方
The University of Manchester
暑期学校介绍
City: Manchester
Language: English
Credits: 0 EC
About
Bayesian methods are widely used to analyse complex data structures, including population counts, time-use data, and other non-standard outcomes. This course introduces both basic and intermediate Bayesian regression methods, with a strong emphasis on practical implementation using R and Stan.
The course is designed for participants with prior experience in regression modelling who wish to develop the skills and confidence to carry out Bayesian estimation, generate predictions, and communicate uncertainty through clear visual summaries, including for hierarchical and spatially structured parameters.
Participants will study generalized linear models (including logistic regression) and multilevel/hierarchical regression frameworks
Course leader
Professor Wendy Olsen and Dr Diego Andrés Pérez Ruiz
Course aim
To:
Specify, estimate, and interpret regression models within a Bayesian framework using R and Stan.
Apply generalized linear models (including logistic, Poisson, and ordinal models) to real-world data.
Understand and diagnose uncertainty in parameter estimates using posterior distributions and Bayesian model comparison tools.
Implement hierarchical and multilevel regression models, including spatial extensions where appropriate.
Produce reproducible, well-documented analytical outputs suitable for research dissemination and professional reporting
Fee info
Fee
900 GBP, Regular
Fee
600 GBP, PGR/Reduced rate
费用
900 GBP
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申请要求
This course is suitable for participants working or researching within data science, social statistics, and related quantitative fields, in either academic or applied settings.
It is particularly appropriate for analysts, data scientists, and researchers who already use regression models and wish to extend their skills to Bayesian estimation, uncertainty quantification, and hierarchical modelling. Participants who primarily use software such as Stata or SAS are very welcome; the course demonstrates how Bayesian regression techniques implemented in R and Stan relate directly to familiar workflows
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