This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
In graphical modelling, a bi-directed graph encodes marginal independences among random variables that are identified with the vertices of the graph. We show how to transform a bi...
- The objective of this paper is to present a graphical-user-interface (GUI) in support of a decision support system (KASER) for machine understanding. In order to provide informat...
Isai Michel Lombera, Jayeshkumar Patel, Stuart Har...