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On probabilistic bounds inspired by interval arithmetic ∗ by

by unknown authors
"... Abstract: A randomized method aimed at evaluation of probabilistic bounds for function values is considered. Stochastic intervals tightly covering ranges of function values with probability close to one are modelled by a randomized method inspired by interval arithmetic. Statistical properties of th ..."
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Abstract: A randomized method aimed at evaluation of probabilistic bounds for function values is considered. Stochastic intervals tightly covering ranges of function values with probability close to one are modelled by a randomized method inspired by interval arithmetic. Statistical properties

Towards Precision of Probabilistic Bounds Propagation

by Helmut Thöne, Ulrich Güntzer, Werner Kießling - PROC. OF THE 8 TH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE , 1992
"... The DUCK-calculus presented here is a recent approach to cope with probabilistic uncertainty in a sound and efficient way. Uncertain rules with bounds for probabilities and explicit conditional independences can be maintained incrementally. The basic inference mechanism relies on local bounds propag ..."
Abstract - Cited by 18 (1 self) - Add to MetaCart
The DUCK-calculus presented here is a recent approach to cope with probabilistic uncertainty in a sound and efficient way. Uncertain rules with bounds for probabilities and explicit conditional independences can be maintained incrementally. The basic inference mechanism relies on local bounds

Improved probabilistic bounds on stopping redundancy

by Junsheng Han, Paul H. Siegel, Alexander Vardy - IEEE TRANS. ON INFORM. THEORY , 2007
"... For a linear code, the stopping redundancy of is defined as the minimum number of check nodes in a Tanner graph T for such that the size of the smallest stopping set in T is equal to the minimum distance of. Han and Siegel recently proved an upper bound on the stopping redundancy of general linear c ..."
Abstract - Cited by 9 (1 self) - Add to MetaCart
codes, using probabilistic analysis. For most code parameters, this bound is the best currently known. In this correspondence, we present several improvements upon this bound.

PROBABILISTIC BOUNDS ON THE VIRTUAL MULTIPLIERS IN DATA ENVELOPMENT ANALYSIS

by Ole B. Olesen, Niels Christian Petersen, Polyhedral Cone Constraints , 1998
"... The paper is concerned with the incorporation of polyhedral cone constraints on the virtual multipliers in DEA. The incorporation of probabilistic bounds on the virtual multipliers based upon a stochastic benchmark vector is demonstrated. The suggested approach involves a stochastic (chance constrai ..."
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The paper is concerned with the incorporation of polyhedral cone constraints on the virtual multipliers in DEA. The incorporation of probabilistic bounds on the virtual multipliers based upon a stochastic benchmark vector is demonstrated. The suggested approach involves a stochastic (chance

Probabilistic bounds on the coefficients of polynomials with only real zeros

by Jim Pitman , 1997
"... The work of Harper and subsequent authors has shown that finite sequences (a0,..., an) arising from combinatorial problems are often such that the polynomial A(z): = n k=0 akz k has only real zeros. Basic examples include rows from the arrays of binomial coefficients, Stirling numbers of the first a ..."
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their probabilistic representation. In combinatorial examples these inequalities yield a number of improvements of known estimates.

Probabilistically Bounded Staleness for Practical Partial Quorums

by Peter Bailis, Shivaram Venkataraman, Joseph M. Hellerstein, Michael J. Franklin, Ion Stoica , 2012
"... ..."
Abstract - Cited by 18 (5 self) - Add to MetaCart
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Quantum complexity theory

by Ethan Bernstein, Umesh Vazirani - in Proc. 25th Annual ACM Symposium on Theory of Computing, ACM , 1993
"... Abstract. In this paper we study quantum computation from a complexity theoretic viewpoint. Our first result is the existence of an efficient universal quantum Turing machine in Deutsch’s model of a quantum Turing machine (QTM) [Proc. Roy. Soc. London Ser. A, 400 (1985), pp. 97–117]. This constructi ..."
Abstract - Cited by 574 (5 self) - Add to MetaCart
the modern (complexity theoretic) formulation of the Church–Turing thesis. We show the existence of a problem, relative to an oracle, that can be solved in polynomial time on a quantum Turing machine, but requires superpolynomial time on a bounded-error probabilistic Turing machine, and thus not in the class

Stochastic Perturbation Theory

by G. W. Stewart , 1988
"... . In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a first-order perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variatio ..."
Abstract - Cited by 907 (36 self) - Add to MetaCart
. In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a first-order perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating

PROBABILISTIC BOUNDED RELATIVE ERROR FOR RARE EVENT SIMULATION LEARNING TECHNIQUES

by C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, A. M. Uhrmacher, Bruno Tuffin, Inria Rennes, Bretagne Atlantique, Ad Ridder, De Boelelaan
"... In rare event simulation, we look for estimators such that the relative accuracy of the output is “controlled” when the rarity is getting more and more critical. Different robustness properties of estimators have been defined in the literature. However, these properties are not adapted to estimators ..."
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to estimators coming from a parametric family for which the optimal parameter is random due to a learning algorithm. These estimators have random accuracy. For this reason, we motivate in this paper the need to define probabilistic robustness properties. We especially focus on the so-called probabilistic

Max-margin Markov networks

by Ben Taskar, Carlos Guestrin, Daphne Koller , 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ..."
Abstract - Cited by 604 (15 self) - Add to MetaCart
independently to each object, losing much useful information. Conversely, probabilistic graphical models, such as Markov networks, can represent correlations between labels, by exploiting problem structure, but cannot handle high-dimensional feature spaces, and lack strong theoretical generalization guarantees
Results 1 - 10 of 3,135