Decision Science with Probabilistic Programming

Make robust optimal decisions with Python!

Mattia Ferrini

Data Science Deep Learning Functional Programming Science

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Generative Models are the Swiss Army Knife for the Decision Scientist. Generative models allow the simulation of scenarios based on different business hypotheses (Bayesian priors). With Probabilistic Programming, decision makers can simulate the impact of business drivers in times of great uncertainty.

Furthermore, Probabilistic Programming Languages provide all the inference tools necessary to identify the assumptions that have most likely generated an outcome. Inference is a statistical tool that enables optimal decision-making based on models that explicitly quantify uncertainty.

Generative models of key optimization parameters are necessary input to Robust Optimization and Stochastic Programming problems.
Python provides all the tools to successfully integrate Probabilitistic Programs with Robust and Stochastic Optimization and therefore cope with high uncertainty in optimization.

Type: Poster session (45 mins); Python level: Intermediate; Domain level: Intermediate

Mattia Ferrini

Mattia has over 15 years of experience in Machine Learning and Applied mathematics.
Mattia is a former hedge fund manager, a startup founder and is currently leading the AI and Mathematical Modelling team at a large consulting company.