Making Decisions in High Dimensions

Most data engineering challenges are concerned with processing large amounts of raw data and converting it to structures that are convenient for counting, machine learning, and visualization. At Generable our challenges are at the other end of the pipeline. We are building high dimensional, generative models, and post-processing model inferences for making decisions under uncertainty. We have not solved this problem in full generality, but we have learned about some of the unique challenges. We will demonstrate our process and highlight these challenges using inferences from Bayesian Survival models that are often used to assess efficacy and safety of new therapies during and after clinical trials.