Bayesian ML and Probabilistic Programming 3: Variational Inference

In this talk, we lay the ground for Markov Chain Monte Carlo (MCMC) methods to inferring the posterior distribution, after looking into the limitations posed by the analytical approach. We demonstrate how MCMC works and use it to solve a Bayesian regression problem and introduce concepts related to causal inference. Before doing so, we have a quick tutorial on using Pyro as a probabilistic programming language.

References

  • Pattern recognition and machine learning, Christopher M. Bishop. (2006)
  • Variational Inference: A Review for Statisticians, David M. Blei, Alp Kucukelbir, Jon D. McAuliffe. Journal of the American Statistical Association (2017)

In this series