2016/02/11 by Satyajit Thakor, Thakor, Satyajit, Terence Chan +3
Computer Science · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Information Theory (cs.IT) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.1602.03618
openalex publication_date 2016/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Characterising the capacity region for a network can be extremely difficult, especially when the sources are dependent. Most existing computable outer bounds are relaxations of the Linear Programming bound. One main challenge to extend linear program bounds to the case of correlated sources is the difficulty (or impossibility) of characterising arbitrary dependencies via entropy functions. This paper tackles the problem by addressing how to use entropy functions to characterise correlation among sources.