Abstract—The classes of all lower/upper bounds on the prob-ability of a finite union of events which are expressed only in terms of the individual event probabilities and the sums of the pairwise event probabilities are considered. The optimal lower and upper bounds in each class are given numerically by solving a linear programming (LP) problem. Furthermore, a suboptimal analytical lower bound is established by solving a relaxed LP problem, which is at least as good as an existing bound due to Kuai, et al. [1]. Note that the new lower bounds can be further improved algorithmically by optimizing them over subsets [2], [3], and can be applied to general estimation problems involving the probability of a finite union. Finally, the new lower/u...
Many probabilistic inference and learning tasks involve summations over exponentially large sets. Re...
AbstractLower bounds on the probability of a union obtained by applying optimal bounds to subsets of...
A central question in information theory is to determine the maximum success probability that can be...
A new lower bound on the probability P (A 1 [ \Delta \Delta \Delta [ AN ) is established in terms of...
AbstractA new lower bound on the probability P(A1∪⋯∪AN) is established in terms of only the individu...
Abstract—We present new results on bounding the probability of a finite union of events, P (⋃N i=1Ai...
New lower bounds on the error probability of block codes with maximum-likelihood decoding are propos...
AbstractThe simple device of maximization over subsets of events can provide substantial improvement...
Given a set of n random events in a probability space, represented by n Bernoulli variables, we cons...
AbstractGiven a sequence of n arbitrary events in a probability space, we assume that the individual...
AbstractWe consider the problem of generating upper bounds for the probability of the union of event...
We present an efficient algorithmic lower bound for the block error rate of linear binary block code...
Abstract—Using an alternate form of the Gaussian probability integral discovered a number of years a...
The performance of specific signal constellations in digital communications problems is often descri...
Abstract. In this thesis, error decoding probability bounds and achiev-able rates for linear and non...
Many probabilistic inference and learning tasks involve summations over exponentially large sets. Re...
AbstractLower bounds on the probability of a union obtained by applying optimal bounds to subsets of...
A central question in information theory is to determine the maximum success probability that can be...
A new lower bound on the probability P (A 1 [ \Delta \Delta \Delta [ AN ) is established in terms of...
AbstractA new lower bound on the probability P(A1∪⋯∪AN) is established in terms of only the individu...
Abstract—We present new results on bounding the probability of a finite union of events, P (⋃N i=1Ai...
New lower bounds on the error probability of block codes with maximum-likelihood decoding are propos...
AbstractThe simple device of maximization over subsets of events can provide substantial improvement...
Given a set of n random events in a probability space, represented by n Bernoulli variables, we cons...
AbstractGiven a sequence of n arbitrary events in a probability space, we assume that the individual...
AbstractWe consider the problem of generating upper bounds for the probability of the union of event...
We present an efficient algorithmic lower bound for the block error rate of linear binary block code...
Abstract—Using an alternate form of the Gaussian probability integral discovered a number of years a...
The performance of specific signal constellations in digital communications problems is often descri...
Abstract. In this thesis, error decoding probability bounds and achiev-able rates for linear and non...
Many probabilistic inference and learning tasks involve summations over exponentially large sets. Re...
AbstractLower bounds on the probability of a union obtained by applying optimal bounds to subsets of...
A central question in information theory is to determine the maximum success probability that can be...