In the last ten years, there have been a number of advancements in the study of Hamiltonian Monte Carlo algorithms that have enabled effective Bayesian statistical computation for much more complicated models than were previously feasible. These algorithmic advancements have been accompanied by a number of open source probabilistic programming packages that make them accessible to programmers and statisticians. PyMC3 is one such package written in Python and supported by NumFOCUS. This workshop will give an introduction to probabilistic programming with PyMC3. No preexisting knowledge of Bayesian statistics is necessary; a working knowledge of Python will be helpful.
Open Source Bayesian Inference in Python with PyMC3
Austin Rochford is a Principal Data Scientist at Monetate. He is a founding member of Monetate Labs, where he does research and development for machine learning-driven personalization products. He is a mathematician by training and a developer of and advocate for open-source data science software.