Origin-destination (OD) modeling facilitates effective demand-responsive public
transportation planning in order to meet emergent needs. Given recent advances in transit …

Predicting human behavior from a small amount of training examples is a challenging
machine learning problem. In this thesis, we introduce the principle of maximum causal …

This paper proposes an intelligent digital twin framework for corrosion fatigue life prediction
and calibration of suspender wires integrated with mechanism-driven, sensor-driven, and …

Modeling a complex system is almost invariably a challenging task. The incorporation of
experimental observations can be used to improve the quality of a model and thus to obtain …

The main content of this review article is first to review the main inference tools using Bayes
rule, the maximum entropy principle (MEP), information theory, relative entropy and the …

Eliminating accidents while maintaining the integrity of the National Airspace System is one
of the central objectives of the Next Generation Air Transportation System. This paper …

This paper is a review of a particular approach to the method of maximum entropy as a
general framework for inference. The discussion emphasizes pragmatic elements in the …

A hybrid method for information fusion combining the maximum entropy (ME) method with
the classical Bayesian network is proposed as the Bayesian-Entropy Network (BEN) in this …

A classic approach for learning Bayesian networks from data is to identify a maximum a
posteriori (MAP) network structure. In the case of discrete Bayesian networks, MAP networks …

In probabilistic (Bayesian) inferences, we typically want to compute properties of the
posterior distribution, describing knowledge of unknown quantities in the context of a …