Resources will be posted here throughout the semester. Diving into this (edifying) material should be of keen interest to those taking the class, but do not consider these formal reading assignments.

Recommended Book

• “Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data “ (Ivezic et al. 2016)

  • Visualization: Ch 1.6
  • Intro to Probability: Ch 3
  • Bayesian Inference: Ch 5

Model Fitting

• “Data analysis recipes: Fitting a model to data” (Hogg et al. 2010). “We go through the many considerations involved in fitting a model to data, using as an example the fit of a straight line to a set of points in a two-dimensional plane.”

• “Data Analysis Recipes: Using Markov Chain Monte Carlo” (Hogg and Foreman-Mackey 2018) “In this primarily pedagogical contribution, we give a brief overview of the most basic MCMC method and some practical advice for the use of MCMC in real inference problems.”

• “Data analysis recipes: Probability calculus for inference” (Hogg 2012) “…I review the rules by which probability distribution functions can (and cannot) be combined. I connect these rules to the operations performed in probabilistic data analysis…The applications of probability calculus in constructing likelihoods, marginalized likelihoods, posterior probabilities, and posterior predictions are all discussed.”

• “Bayesian Methods for Hackers” (Cameron Davidson-Pilon) “An intro to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view.”

Periodograms

• “Understanding the Lomb-Scargle Periodogram” (VanderPlas 2017) “This paper presents a conceptual introduction to the Lomb-Scargle periodogram and important practical considerations for its use.”

Gaia Variable Star References

• “Gaia Data Release 1. The Cepheid and RR Lyrae star pipeline and its application to the south ecliptic pole region”

• “Gaia Data Release 2. Specific characterisation and validation of all-sky Cepheids and RR Lyrae stars”

• “Gaia Data Release 2. Summary of the variability processing and analysis results”

Blogs/Online Material of Interest

• “Warning Signs in Experimental Design and Interpretation” (P. Norvig). Enumerates many of the pitfalls in hypothesis testing, on correlation vs causation, reproducibility, and bias. Good reference set of statistics book at the end of the blog.

Books

• “The Visual Display of Quantitative Information” (E. Tufte). Called “The century’s best book on statistical graphics” this the go-to reference for understanding the core principles of information display.

Packages and Code

• “astroquery: An Astronomical Web-Querying Package in Python” (A. Ginsburg et al.). Main Python-based query tool for (remote) astronomical databases.

• “The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package” Suite of astronomical Python packages.

• “pyMC3” “Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference — including minibatch-ADVI for scaling to large datasets — or using Gaussian processes to build Bayesian nonparametric models.”