Reading List for GEOG0125

Contact me via email () if you are having problems securing one or any of these recommended books from the UCL library or elsewhere. You can access some of the share reading materials [HERE]

Week 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. Book: [Theory] Graham, A. (1994). Statistics. Chapters 13: Probability Models. Pages 226-251.

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

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

Week 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.

Week 3: Bayesian Generalised Additive Models (GAMs)

  1. Online: [Tutorial]. Fitting GAMs with brms: Part 1 a Simple GAM. LINK: https://fromthebottomoftheheap.net/2018/04/21/fitting-gams-with-brms/

  2. Online: [Tutorial] Chapter 2: Interpreting and Visualizing GAMs. LINK: https://noamross.github.io/gams-in-r-course/chapter2

  3. Book: [Theory] Wood, S.N. (2017). Generalized Additive Models: An Introduction with R (2nd Edition). Chapter 4: Introducing GAMs. Pages 161-191.

  4. Book: [Theory and Applications] Wood, S.N. (2017). Generalized Additive Models: An Introduction with R (2nd Edition). Chapter 7: GAMs in Practice. Pages 325-398.

Week 7: 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.

Week 8: Spatial Intrinsic Conditional Autoregressive Modelling (ICAR)

  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

Week 9: Bayesian Updating and Spatiotemporal Modelling

  1. Book: [R-INLA Tutorials] Moraga, P. (2020). Geospatial Health Data: Modeling and Vizualisation with R-INLA and Shiny. Chapters 5: Areal Data. Pages 53-74.

  2. Book: [R-INLA Tutorials] Moraga, P. (2020). Geospatial Health Data: Modeling and Vizualisation with R-INLA and Shiny. Chapters 6: Spatial modeling of areal. Lip cancer in Scotland. Pages 75-88.

  3. Book: [R-INLA Tutorials] Moraga, P. (2020). Geospatial Health Data: Modeling and Vizualisation with R-INLA and Shiny. Chapters 7: Spatio-temporal modeling of areal data. Lung cancer in Ohio. Pages 93-108.

  4. Book: [R-INLA Methodology] Blangiardo, M, & Cameletti, M. (2015). Spatial and Spatio-temporal Bayesian Models in R-INLA. Chapters 6: Spatial modeling. Sections 6.1-6.3, Pages 173-192.

  5. Book: [R-INLA Methodology] Blangiardo, M, & Cameletti, M. (2015). Spatial and Spatio-temporal Bayesian Models in R-INLA. Chapters 7: Spatio-temporal models. Sections 7.1-7.2, Pages 253-258.

Week 10: Study Design, Research Methodology & Revision

[Not Applicable]

Excellent & Incredibly Useful Tutorial Videos for Learning Stan Code

  1. Bayesian Inference with Stan Episode 1: Motivation [Video]
  2. Bayesian Inference with Stan Episode 2: Theory and concepts [Video]
  3. Bayesian Inference with Stan Episode 3: Linear Regression [Video]
  4. Bayesian Inference with Stan Episode 4: Logistic Regression [Video]

4-Day Summer Workshop: Introduction to Bayesian Inference and Modelling [2022/23]

UCL SODA CPD Workshop [SOURCE]

  1. Day 1: Introduction to Bayesian Inference using Stan [Lecture Video] [Live Demonstration]
  2. Day 2: Bayesian Generalised Linear Models using Stan [Lecture Video] [Live Demonstration]
  3. Day 3: Bayesian Generalised Hierarchical Regression Models [Lecture Video (Part 1)][Lecture Video (Part 2)][Live Demonstration]
  4. Day 4: Bayesian Spatial Modelling for Areal Data in Stan [Lecture Video] [Live Demonstration]