Data Tech

In several machine learning modeling frameworks, results of simulations can look like nodes of a graph and it is up to the user to take the inputs and outputs of models and connect them to each other. It is also common for data to be missing, and one would like to to link these nodes and treat them in a system as a coherent whole.

Approaching these models as a computational graph allows one to use a probabilistic programming framework. This talk will address the questions of "why?", "what?", an "how?" to use probabilistic programming, and offer two examples of probabilistic programming applied to problems with large data sets to illustrate the efficacy and effectiveness of the approach.