Reading For Parrots

A compendium of resources to check whenever I feel the urge to do or learn something but don’t want to figure out what. There is enough here to keep me engaged for a very long time.

Applied Statistics—

Good resources are awesome-neural-sbi and Cosma Shalizi’s notebook.

Data Analysis Recipes: Using Markov Chain Monte Carlo by David Hogg and Dan Foreman-Mackey; kind of a standard reference introduction to Metropolis-Hastings MCMC methods from the folks that brought us emcee.
A Conceptual Introduction to Hamiltonian Monte Carlo, Michael Betancourt; does what it says on the can.
Betanalpha by Michael Betancourt; former physicist, current statistician, and suspected Stan developer(?). Writes a blog with excellent introductions to probabilistic programming with code snippets.
Simulator-Based Inference with WALDO, Masseranno et. al, 2022
Neural Methods for Amortized Inference, Zammit-Mangion et. al, 2024; a quite recent review paper on simulation-based inference with an emphasis on neural networks. Covers neural posterior estimation.
Statistical inference for dynamical systems: A review, Kevin McGoff, Sayan Mukherjee, and Natesh Pillai, 2015; does what it says on the can.
Three Talks by Eric Vanden-Eijden; who studies extreme events, stochastic systems, etc. at NYU Courant.
Applied Stochastic Analysis by E, Li, and Vanden-Eijden; great book with fun exercises.

Non-Equilibrium Thermodynamics

Stochastic thermodynamics, fluctuation theorems, and molecular machines, Udo Seifert, 2012; the standard review paper in the field. Covers fluctuation theorems, stochastic entropy, etc.
Stochastic Thermodynamics: An Introduction by Luca Peliti and Simone Pigolotti; similar in scope to Seifert+2012 but much more comprehensive.

Fluid Dynamics—

Lecture Notes in Stellar Oscillations by Jørgen Christensen-Daalsgard; a classic, very useful for learning asteroseismology. Notebook #8 has dozens of pages of notes on this book.
Classic and Historic Papers in Geophysical Fluid Dynamics; maintained by Geoff Vallis, whose textbook I’ve read parts of and is definitely worth the effort.
Lecture notes: Astrophysical Fluid Dynamics by Gordon Ogilvie; based on the corresponding course for Part III of the Mathematical Tripos.

Causal Inference—

Counterfactuals and Causal Inference by Steven L. Morgan and Christopher Winship; comes recommended and will need to look into it. This is more suited toward social scientists, but then again I have no real reason to be learning causal inference techniques. I took a class with Steven Morgan; he’s a good professor.
Causal Inference: What If by Miguel Hernan and James Robins; well written and intuitive as far as I’ve read so far. More enjoyable to read than most statistics books.
Causal Machine Learning: A Survey and Open Problems, Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva, 2022; very long explainer of causal machine learning.

Blogs—

Pluralistic by Cory Doctorow; sort of a blog’s blog. I like his novels.
Statistical Modeling, Causal Inference, and Social Science by Andrew Gelman et. al; A statistics blog oriented toward more traditional practitioners (e.g. public health, policy, economics). Comes highly recommended by people who are much smarter than me.
Azimuth by John Carlos Baez; mostly a science blog but does seem to have some non-science content. Obviously John Carlos Baez is someone worth reading.
Programmable Mutter by Henry Farrell; professor of political science at SNF Agora on AI and the tech industry, mixed with a brain-tickling penchant for magic and cosmic horror.
Crooked Timber; I haven’t figured this one out yet. Seems to be a remnant of a time when the world was smaller, but it’s still active. Tech industry, culture, and American politics.