Probabilistic Programming with Tensorflow Probability



Probabilistic programming is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. It represents an attempt to unify probabilistic modeling and traditional general-purpose programming in order to make the former easier and more widely applicable. The session will be a beginner-friendly tutorial on probabilistic programming using Tensorflow Probability (TFP).

Basic Outline

  • Philosophy of Bayesian Inference (3 mins)
  • Setup/Installation for Tensorflow Probability (2 mins)
  • Probabilistic programming in action (15 mins)
    • Explaining posterior probability with example of coin toss (5 mins)
    • Real world example of building Bayesian model (10 mins)
  • Epilogue: current applications and limitations (2 mins)
  • Questions and Answers (QNA) (3 mins)


  • Familiarity with python syntax
  • Google Colab

Video URL:

Speaker Info:

Hi, I am Anmol Jindal, I work as a data scientist at Intelligence Node (Mumbai). I have worked on Computer Vision, AI-powered Search engines and some other fun stuff. I have previously presented at Pycon India 2020.

Speaker Links:

Section: Data Science, Machine Learning and AI
Type: Talks
Target Audience: Beginner
Last Updated: