A non-Shannon-type conditional inequality of information quantities

Given n discrete random variables Ω={X₁,…,X_n}, associated with any subset α of {1,2,…,n}, there is a joint entropy H(X_α) where X_α={X_i: i∈α}. This can be viewed as a function defined on 2^{1,2,…,n} taking values in [0, +∞). We call this function the entropy function of Ω. The nonnegativity of the joint entropies implies that this function is nonnegative; the nonnegativity of the conditional joint entropies implies that this function is nondecreasing; and the nonnegativity of the conditional mutual information implies that this function is two-alternative. These properties are the so-called basic information inequalities of Shannon´s information measures. An entropy function can be viewed as a 2ⁿ -1-dimensional vector where the coordinates are indexed by the subsets of the ground set {1,2,…,n}. As introduced by Yeng (see ibid., vol.43, no.6, p.1923-34, 1997) Γ_n stands for the cone in IR(2ⁿ-1) consisting of all vectors which have all these properties. Let Γ_n* be the set of all 2ⁿ -1-dimensional vectors which correspond to the entropy functions of some sets of n discrete random variables. A fundamental information-theoretic problem is whether or not Γ¯_n*=Γ_n. Here Γ¯_n * stands for the closure of the set Γ_n*. We show that Γ¯_n* is a convex cone, Γ₂*=Γ₂, Γ₃*≠Γ₃, but Γ¯₃ *=Γ₃. For four random variables, we have discovered a conditional inequality which is not implied by the basic information inequalities of the same set of random variables. This lends an evidence to the plausible conjecture that Γ¯_n*≠Γ_n for n>3