#Pymc3

In a previous post (https://lnkd.in/dZvmsRm), I looked at the available data for the infected cases in the United States as a time-series, modeling this as a compartmental probabilistic model and inferring the disease parameters such as R0 using Bayesian estimation. However, we can use the case counts from several countries and use Bayesian hierarchical models to extend this work and better estimate R0. In this post I illustrate how we can do exactly that using PyMC3. [Read More]

These are the slides for the talk given in the Data Science Lounge at the Data+AI Summit, 2020. Introduction This post is a demonstration of how to use PyMC3 to infer the disease parameters for COVID-19. PyMC3 is a probablistic programming framework that is used for Bayesian modeling. It accomplishes this through both Markov Chain Monte Carlo (MCMC) and Variational Inference methods. The work here looks at using the currently available data for the infected cases in the United States as a time-series and attempts to model this using a compartmental model. [Read More]