Reading List

Contact me via email a.musah@ucl.ac.uk if you are having problems securing one of these recommended books. Check these downloadable ‘Easter Eggs’ in this Google Drive Repository [LINK].

Day 1: Introduction to Probability Distributions

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

  • Book: [Theory] Donovan, T.M., & Mickey, R.M. (2019). Bayesian Statistics for Beginners: A Step-by-Step Approach. Chapters 1: Introduction to Probability. Pages 3-29.

  • Book: [Theory] Donovan, T.M., & Mickey, R.M. (2019). Bayesian Statistics for Beginners: A Step-by-Step Approach. Chapters 3: Probability Functions. Pages 87-132.

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

Day 2: Introduction to Bayesian Inference

  • Book: [Theory] Donovan, T.M., & Mickey, R.M. (2019). Bayesian Statistics for Beginners: A Step-by-Step Approach. Chapters 2: Bayes’ Theorem and Bayesian Inference. Pages 3-29.

Day 3: Bayesian Generalised Linear Models (GLMs)

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

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

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

  • Book: [Stan Programming] Lambert, B. (2018). A Student’s Guide to Bayesian Statistics. Chapters 18: Linear Regression Models. Pages 453-466.

Day 4: Bayesian Hierarchical Regression Models

  • Article: [Stan Programming] 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

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

Day 5: Bayesian Spatial Modelling for Areal Data in Stan

  • Article: [Application] 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.

  • Article: [Application] 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

  • 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

  • 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

  • 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.

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

  • Article: [History] 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