Reading assignments

Week 1: Chapters 1 and 2 in Sivia

Eddy, What is Bayesian statistics?
Week 2: Chapter 3 in Sivia

Rougier, et al. Ten simple rules for better figures; see also original Python code to generate figures in the paper.

Wickham, Tidy Data
Week 3: Chapters 3 and 5.1 in Sivia

Chapter 23 in MacKay (optional)
Week 4: Chapter 4 Sivia
Week 5: Chapter 29 in MacKay

A nice how-to by Hogg and Foreman-Mackey

Chapter 12 in Gregory is also useful
Week 6: Betancourt, A Conceptual Introduction to Hamiltonian Monte Carlo is worth reading, and/or watch this video.
Chapter 8 in Sivia
Chapter 23 in MacKay
Week 7: Vehtari, Gelman, and gabry, Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.

Source papers for data sets

Kleinteich and Gorb, Tongue adhesion in the horned frog Ceratophrys sp., Sci. Rep., 4, 5225, 2014
Gardner, Zanic, et al., Depolymerizing kinesins Kip3 and MCAK shape cellular microtubule architecture by differential control of catastrophe, Cell, 147, 1092-1103, 2011
Prober, et al., Hypocretin/Orexin overexpression induces an insomnia-like phenotype in zebrafish, J. Neurosci., 26, 13400-13410, 2006
Gandhi, et al., Melatonin Is required for the circadian regulation of sleep Neuron, 85, 1193-1199, 2015
Good, et al., Cytoplasmic volume modulates spindle size during embryogenesis, Science, 342, 856-860, 2013
Reeves, Trisnadi, et al., Dorsal-ventral gene expression in the Drosophila embryo reflects the dynamics and precision of the Dorsal nuclear gradient, Dev. Cell, 22, 544-557, 2012
Singer, et al., Dynamic heterogeneity and DNA methylation in embryonic stem cells, Molec. Cell, 55, 319-331, 2014

D. S. Sivia, Data Analysis: A Bayesian Tutorial, 2nd Ed., Oxford University Press, 2006. This is our main text for the course. It is also available in electronic form (though you cannot download the entire book as a PDF) here.
Gonzalez and Woods, Digital Image Processing, 3rd Ed., Prentice-Hall, 2007. This is the standard book for the basics of image processing.
Phil Gregory, Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press, 2005. This goes into more depth on many of the topics covered in Sivia. It also has an addendum.
David MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003. This excellent book beautifiully ties together three related areas of science in a clear, pedagogical, and sometimes humorous manner. (Available for free online from the link.)

The Think X series by Allen Downey are excellent pedagogical books for learning basic programming and statistics. They are available for free online. The most useful are Think Stats, Think Bayes, and Think Python.
Bradley Efron and Trevor Hastie, Computer Age Statistical Inference, Cambridge University Press, 2016. This book gives a nice historical perspective on the development of modern tools, which provides a nice overview of how everything fits together.
Richard McElreath, Statistical Rethinking, CRC Press, 2015. This book is a clear, delightful read about Bayesian statistics.
Blitzstein and Hwang, Introduction to Probability, Chapman and Hall/CRC, 2014. This is a beautiful book explaining fundamental concepts in probability.
Kinder and Nelson, A Student's Guide to Python for Physical Modeling, Princeton University Press, 2015. This is a nice book to have by your side while getting your feet wet with Python.