Bijectors.jl: Transforming probability distributions in Julia
Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning, e.g. constrained-to-unconstrained transformations of distributions for use in Hamiltonian Monte Carlo or constructing flexible and learnable densities such as normalizing flows.
In this talk we'll have a look at how we can use Bijectors.jl to do all of the above and more!
The slides can be bound at https://torfjelde.github.io/presentations/juliacon-2020-bijectors/.
Hey. My name is Tor, like the onion browser or the norse god. Neither are fitting; I'm not a man of many layers nor am I long-haired and muscular. I'm currently working as a research assistant in the Machine Learning group at University of Cambridge where I spend time doing research and working on Turing.jl and it's related packages, e.g Bijectors.jl. It's all related to probabilities, and it's all good fun.
And like you, I of course loooove Julia!