TY - JOUR

T1 - Mutual Information Bounds via Adjacency Events

AU - Han, Yanjun

AU - Ordentlich, Or

AU - Shayevitz, Ofer

N1 - Publisher Copyright:
© 1963-2012 IEEE.

PY - 2016/11

Y1 - 2016/11

N2 - The mutual information between two jointly distributed random variables X and Y is a functional of the joint distribution PXY, which is sometimes difficult to handle or estimate. A coarser description of the statistical behavior of (X, Y) is given by the marginal distributions PX, PY and the adjacency relation induced by the joint distribution, where x and y are adjacent if P(x, y) > 0. We derive a lower bound on the mutual information in terms of these entities. The bound is obtained by viewing the channel from X to Y as a probability distribution on a set of possible actions, where an action determines the output for any possible input, and is independently drawn. We also provide an alternative proof based on convex optimization that yields a generally tighter bound. Finally, we derive an upper bound on the mutual information in terms of adjacency events between the action and the pair (X, Y), where in this case, an action a and a pair (x, y) are adjacent if y = a(x). As an example, we apply our bounds to the binary deletion channel and show that for the special case of an independent identically distributed input distribution and a range of deletion probabilities, our lower and upper bounds both outperform the best known bounds for the mutual information.

AB - The mutual information between two jointly distributed random variables X and Y is a functional of the joint distribution PXY, which is sometimes difficult to handle or estimate. A coarser description of the statistical behavior of (X, Y) is given by the marginal distributions PX, PY and the adjacency relation induced by the joint distribution, where x and y are adjacent if P(x, y) > 0. We derive a lower bound on the mutual information in terms of these entities. The bound is obtained by viewing the channel from X to Y as a probability distribution on a set of possible actions, where an action determines the output for any possible input, and is independently drawn. We also provide an alternative proof based on convex optimization that yields a generally tighter bound. Finally, we derive an upper bound on the mutual information in terms of adjacency events between the action and the pair (X, Y), where in this case, an action a and a pair (x, y) are adjacent if y = a(x). As an example, we apply our bounds to the binary deletion channel and show that for the special case of an independent identically distributed input distribution and a range of deletion probabilities, our lower and upper bounds both outperform the best known bounds for the mutual information.

KW - Mutual information bounds

KW - alternating minimization

KW - deletion channel

KW - functional representation

UR - http://www.scopus.com/inward/record.url?scp=85027352553&partnerID=8YFLogxK

U2 - 10.1109/TIT.2016.2609390

DO - 10.1109/TIT.2016.2609390

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AN - SCOPUS:85027352553

VL - 62

SP - 6068

EP - 6080

JO - IEEE Transactions on Information Theory

JF - IEEE Transactions on Information Theory

SN - 0018-9448

IS - 11

M1 - 7567587

ER -