Bayesian Machine Learning meets Formal Methods: An application to spatio-temporal data

TL;DR

This paper introduces a novel interdisciplinary framework combining Bayesian predictive inference with formal methods, specifically spatio-temporal logic, to evaluate and compare models based on their ability to predict property satisfaction in urban mobility data.

Contribution

It presents a new methodology integrating Bayesian inference with formal property verification using spatio-temporal logic for predictive modeling.

Findings

Successfully applied to urban mobility data in Milan

Enabled property-based model comparison

Demonstrated effectiveness in predicting city crowdedness

Abstract

We propose an interdisciplinary framework that combines Bayesian predictive inference, a well-established tool in Machine Learning, with Formal Methods rooted in the computer science community. Bayesian predictive inference allows for coherently incorporating uncertainty about unknown quantities by making use of methods or models that produce predictive distributions, which in turn inform decision problems. By formalizing these decision problems into properties with the help of spatio-temporal logic, we can formulate and predict how likely such properties are to be satisfied in the future at a certain location. Moreover, we can leverage our methodology to evaluate and compare models directly on their ability to predict the satisfaction of application-driven properties. The approach is illustrated in an urban mobility application, where the crowdedness in the center of Milan is proxied by aggregated mobile phone traffic data. We specify several desirable spatio-temporal properties related to city crowdedness such as a fault-tolerant network or the reachability of hospitals. After verifying these properties on draws from the posterior predictive distributions, we compare several spatio-temporal Bayesian models based on their overall and property-based predictive performance.