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We study the change of multivariate mutual information among a set of random variables when some common randomness is added to or removed from a subset of the random variables. This is formulated more precisely as two new multiterminal secret key agreement problems that, respectively, ask how one can increase the secrecy capacity efficiently by adding common randomness to a small subset of users, and how one can simplify the source model by removing redundant common randomness that does not contribute to the secrecy capacity. Characterizations and strongly polynomial-time computations are derived for the rates of change, maximum usable increment, and redundancy. These results can be applied to study the communication complexity for secret key agreement.

Many of the classical and recent relations between information and estimation in the presence of Gaussian noise can be viewed as identities between expectations of random quantities. These include the I-MMSE relationship of Guo et al.; the relative entropy and mismatched estimation relationship of Verd\'{u}; the relationship between causal estimation and mutual information of Duncan, and its extension to the presence of feedback by Kadota et al.; the relationship between causal and non-casual estimation of Guo et al., and its mismatched version of Weissman. We dispense with the expectations and explore the nature of the pointwise relations between the respective random quantities. The pointwise relations that we find are as succinctly stated as - and give considerable insight into - the original expectation identities. As an illustration of our results, consider Duncan's 1970 discovery that the mutual information is equal to the causal MMSE in the AWGN channel, which can equivalently be expressed saying that the difference between the input-output information density and half the causal estimation error is a zero mean random variable (regardless of the distribution of the channel input). We characterize this random variable explicitly, rather than merely its expectation. Classical estimation and information theoretic quantities emerge with new and surprising roles. For example, the variance of this random variable turns out to be given by the causal MMSE (which, in turn, is equal to the mutual information by Duncan's result)., Comment: 31 pages, 2 figures, submitted to IEEE Transactions on Information Theory

In this paper, some relations between estimation and mutual information are given by expressing two mutual information calculations in terms of two distinct estimation errors. Specifically the mutual information between a stochastic signal and a pure jump Levy process whose rate function depends on the signal is expressed in terms of a filtering error and the rate of change of this mutual information with respect to a parameter multiplying the rate function of the Levy process is expressed in terms of a smoothing error. These results generalize the analogous mutual information results for some Gaussian noise processes with additive stochastic signals.

A popular way to measure the degree of dependence between two random objects is by their mutual information, defined as the divergence between the joint and product-of-marginal distributions. We investigate an alternative measure of dependence: the lautum information defined as the divergence between the product-of-marginal and joint distributions, i.e., swapping the arguments in the definition of mutual information. Some operational characterizations and properties are provided for this alternative measure of information.

A relationship between information theory and estimation theory was recently shown for the Gaussian channel, relating the derivative of mutual information with the minimum mean-square error. This paper generalizes the link between information theory and estimation theory to arbitrary channels, giving representations of the derivative of mutual information as a function of the conditional marginal input distributions given the outputs. We illustrate the use of this representation in the efficient numerical computation of the mutual information achieved by inputs such as specific codes or natural language

A basic mathematical function that conjoins the two key conceptions of mutual information and Bayes risk is defined. Based on that function, some asymptotic theorems that verify an important implication of mutual information in the context of practical Bayesian decisionmaking are proven. >

The mutual information plays a central role in communication theory; its general expression for stochastic processes has already been derived. Based on this expression, this correspondence provides an alternative representation of the mutual information for Gaussian processes which is useful in estimation problems.