Probabilistic Programming & Bayesian Inference
Live Training, November 15th, 12 PM ET
Duration: 3 Hours, November 15h, 1 PM ET
Lara Kattan
PP is the idea that we can use computer code to build probability distributions
Theory of the primitives in probabilistic programming and how we can build models out of distributions
Classically, we had simulations, but they run in only one direction: get data input and move it according to assumptions of parameters and get a prediction
Bayesian inference adds another direction: use the data to go back and pick one of many possible parameters as the most likely to have created the data (posterior distributions)
Use Bayes' theorem to find the most likely values of the model parameters
We'll work through actual examples of models using PyMC3, including hierarchical models
Solving Bayes’ theorem in practice requires taking integrals, and if we don’t want to do integrals by hand, we need to use numerical solution methods
From the package authors: "[PyMC3 is an ]open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed"
Basic Python and machine learning, sklearn, some stats and probability