Resources

A series of websites, papers and packages that I’ve found particularly helpful for the highlighted topics.

Gamma-Ray Analysis

• 🖥️ Gammapy
  • Gammapy is an open-source Python package of data analysis for gamma-ray astronomy.
• 🖥️ GammaBayes
  • GammaBayes is an open-source Python package of Dayesian data analysis in gamma-ray astronomy. (#selfplug)
• 🖥️ GALPROP
  • GALPROP is a numerical code for calculating the propagation of relativistic charged particles and the diffuse emissions produced during their propagation.

Dark matter codes

• 🖥️ MicrOMEGAs
  • A code for the calculation of Dark Matter Properties including the relic density, direct and indirect rates in a general supersymmetric model and other models of New Physics
• 🖥️ DarkSUSY
  • DarkSUSY is a flexible and modular Fortran package to calculate observables for a variety of dark matter candidates.

Stochastic Sampling Methods

MC Sampling

• 🖥️ emcee
  • emcee is an MIT licensed pure-Python implementation of Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler
• 🖥️ pyMC
  • PyMC is a probabilistic programming library for Python that allows users to build Bayesian models with a simple Python API and fit them using Markov chain Monte Carlo (MCMC) methods.
• 📚 A Conceptual Introduction to Markov Chain Monte Carlo Methods by Joshua S. Speagle
  • Speagle outlines from a pretty low foundational knowledge how MCMC methods work

Nested Sampling

• 📚 Nested sampling for physical scientists by Greg Ashton et al.
  • Nice intro to nested sampling, particularly how prior volume is related to likelihood density values
• 🖥️ dynesty by Joshua S. Speagle
  • Easy and efficient python code to use nested sampling
• 📚 dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences by Joshua S. Speagle
  • A denser introduction to nested sampling than the above paper, but does introduce the algorithms in more depth and outlines the features of the dynesty package

Variational Inference

• 📚 Variational Inference: A Review for Statisticians by David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
  • As in the title it’s a nice review of variational inference and has a particularly nice intro into normalising flows

Normalising Flows

• 📚 Normalizing Flows: An Introduction and Review of Current Methods by Ivan Kobyzev, Simon J.D. Prince, Marcus A. Brubaker
  • Has a great walkthrough for simple to more complex normalising flow methods
• 🖥️ FlowJAX by Daniel Ward, Tennessee Hickling, Matthew Mould, Gilad Turok
  • Wicked fast normalising flows, in particular the ability to perform variational inference with them

Misc

• 🌐 Academic Pages GitHub
  • What I used to make this website
• 📚 Andy Casey’s Introduction to Data Analysis
  • Best place I’ve ever found for an introduction to Bayesian analysis methods
• 🖥️ JAX
  • JAX is a library for array-oriented numerical computation (à la NumPy), with automatic differentiation and JIT compilation to enable high-performance machine learning research.
• 🖥️ CuPy
  • NumPy/SciPy-compatible Array Library for GPU-accelerated Computing with Python
• 🖥️ Paperpile
  • Nice reference manager with Android app for those that like reading papers on the go/on tablets

• 🖥️ PyTorch Tutorials

• 🖥️ Pyro Documentation