## Automatic Differentiation
- Forward or Reverse Mode Automatic Differentiation: What’s the Difference? — Schrijvers et al
- Beautiful Differentiation — Elliott
- Functional Differentiation of Computer Programs — Karczmarczuk — old paper but potentially useful for background
## Probabilistic Programming
Moving towards likelihood-free programming. This means new distributions are written as models which specify how to generate samples from that distribution. More interestingly, we do not need to know the probability density function.
This means we can compose distributions/random variables with usual arithmetic functions and not worry about what happens to the density function!
See #6 for inference methods