Reading List

Day 1: Introduction to Bayesian Inference

  1. Book: [Theory] Slater, M. (2022). Bayesian Methods in Statistics: From Concepts to Practice. Chapters 1: Probability. Pages 1-15.

  2. Book: [Theory] Slater, M. (2022). Bayesian Methods in Statistics: From Concepts to Practice. Chapters 2: Probability Distributions (Section: Some common distributions). Pages 24-45.

  3. Book: [Theory] Donovan, T.M., & Mickey, R.M. (2019). Bayesian Statistics for Beginners: A Step-by-Step Approach. Chapters 3: Bayes’ Theorem. Pages 29-36.

  4. Article: [About Stan] Carpenter, B., Gelman, A., et al (2019). Stan: A Probabilistic Programming Language. J Stat Soft. DOI: 10.18637/jss.v076.i01.

  5. Book: [Stan programming] Lambert, B. (2018). A Student’s Guide to Bayesian Statistics. Chapters 16: Stan.

Day 2: Bayesian Generalised Linear Models (GLMs)

  1. Book: [Theory] Slater, M. (2022). Bayesian Methods in Statistics: From Concepts to Practice. Chapters 5: General Models. Pages 114-151.

  2. Article: [About regression models and formulation] Baldwin, S.A., & Larson, M.J. (2017). An introduction to using Bayesian linear regression with clinical data. Behaviour Research and Therapy. 98:58-75. DOI: 10.1016/j.brat.2016.12.016.

  3. Book: [Theory] Gelman, A et al. (2014). Bayesian Data Analysis (3rd Edition).Chapters 14: Introduction to Regression Models. Pages 353-378.

Day 3: Bayesian Hierarchical Regression Models

  1. Article: [Tutorial in Stan] Sorensen, T., & Vasishth, S. (2016). Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists. Tutorials in Quantitative Methods for Psychology. 12(3):175-200. DOI: 10.20982/tqmp.12.3.p175

  2. Book: [Theory] Gelman, A et al. (2014). Bayesian Data Analysis (3rd Edition).Chapters 15: Hierarchical Linear Models. Pages 381-402.

Day 4: Bayesian Spatial Modelling for Areal Data in Stan

  1. Article: [Methodology] Li, L. et al (2022). An ecological study exploring the geospatial associations between socioeconomic deprivation and fire-related dwelling casualties in the England (2010–2019). Applied Geography. 144(1027718). DOI: 10.1016/j.apgeog.2022.102718.

  2. Article: [Theory] Morris, M. et al (2019). Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan. Spatial and Spatio-temporal Epidemiology. 31(100301). DOI: 10.1016/j.sste.2019.100301

  3. Online Tutorials: [Stan Programming] Morris, M. et al (2019). Spatial Models in Stan: Intrinsic Auto-Regressive Models for Areal Data. URL: https://mc-stan.org/users/documentation/case-studies/icar_stan.html

  4. Article: [Methodology] Gomez, M.J. et al (2023). Bayesian spatial modeling of childhood overweight and obesity prevalence in Costa Rica. BMC Public Health. 23(651). DOI:10.1186/s12889-023-15486-1

  5. Article: [History] Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society. Series B (Methodological) (1974): 192-236.

  6. Article: [History] Besag, J. & Kooperberg, K. (1995) “On conditional and intrinsic autoregression. Biometrika. 733-746.

  7. Article: [History, how the ICAR model was improved] Riebler, A., et al (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical methods in medical research. 25(4): 1145-1165. DOI: 10.1177/0962280216660421