We resolve three long-standing open problems, namely the (algorithmic) decidability of
network coding, the decidability of conditional information inequalities, and the decidability …

We present a versatile automated theorem proving framework capable of automated
discovery, simplification and proofs of inner and outer bounds in network information theory …

Given a Bayesian network structure (directed acyclic graph), the celebrated d-separation
algorithm efficiently determines whether the network structure implies a given conditional …

The study of the fundamental limits of information systems is a central theme in information
theory. Both the traditional analytical approach and the recently proposed computational …

We show that the essential properties of entropy (monotonicity, additivity and subadditivity)
are consequences of entropy being a monoidal natural transformation from the under …

Information diagram and the I-measure are useful mnemonics where random variables are
treated as sets, and entropy and mutual information are treated as a signed measure. The …

Proving linear inequalities and identities of Shannon's information measures, possibly with
linear constraints on the information measures, is an important problem in information …

The proof of information inequalities and identities under linear constraints on the
information measures is an important problem in information theory. For this purpose, ITIP …

Recently, Ledoux, Nair, and Wang proved that the Fisher information along the heat flow is
log-convex in dimension one, that is d 2 dt 2 log (I (X t))≥ 0 for n= 1, where X t is a random …