Readings

Reading assignments

Week 1
Rougier, et al. Ten simple rules for better figures; see also original Python code to generate figures in the paper.
Week 2
Wickham, Tidy Data

Wickham, Split-apply-combine
Week 3
Chapter 23 in MacKay
Week 4
Dan White's intro to image processing
Week 5
Sections 10.2, 10.3, 10.4, 11.1, and 11.2 of Efron and Tibshirani

Nuzzo, Statistical errors
Week 6
Eddy, What is Bayesian statistics?
Week 7
Betancourt, A Conceptual Introduction to Hamiltonian Monte Carlo and/or watch this video
Week 8
Vehtari, Gelman, and Gabry, Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.
Week 9
Betancourt, Towards a Principled Bayesian Workflow.
Talts, et al., Validating Bayesian Inference Algorithms with Simulation-Based Calibration (optional).

Source papers for data sets


Books