Results 1  10
of
3,135
On probabilistic bounds inspired by interval arithmetic ∗ by
"... 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 ..."
Abstract
 Add to MetaCart
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
 PROC. OF THE 8 TH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 1992
"... The DUCKcalculus 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 DUCKcalculus 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
 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
, 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 ..."
Abstract
 Add to MetaCart
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
, 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 ..."
Abstract

Cited by 33 (0 self)
 Add to MetaCart
their probabilistic representation. In combinatorial examples these inequalities yield a number of improvements of known estimates.
Quantum complexity theory
 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 boundederror probabilistic Turing machine, and thus not in the class
Stochastic Perturbation Theory
, 1988
"... . In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a firstorder 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 firstorder 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
"... 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 ..."
Abstract
 Add to MetaCart
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 socalled probabilistic
Maxmargin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased 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 highdimensional feature spaces, and lack strong theoretical generalization guarantees
Results 1  10
of
3,135