Junpeng Lao: A Hitchhiker's Guide to designing a Bayesian library in Python | PyData Córdoba

With modern automatic differentiation libraries like Tensorflow, Jax, autograd, Pytorch, Theano, and more (insert your favorite autograd library here), writing a Bayesian library (or aspiringly, a Probabilistic programming language) seems could not be easier. So, what are the challenges? In this talk, I will speak about designing a Bayesian computation library using PyMC3 as an example, and share some stories about our (now) two iteration of designing PyMC4, with some anecdotes on comparing different Bayesian libraries, choosing a new computational backend, TF1 to TF2 transition and graph modification. In a way, this talk is NOT a tutorial of how to design a Bayesian library, but the opposite: I will try to convince you not to write one, unless you want to deal with 3 types of shape issues, find 10 alternative ways to rewriting control flows, and learn a lot of tricks to handle edge cases that could quickly goes obscure. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...