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15,443
SmallBias Probability Spaces: Efficient Constructions and Applications
 SIAM J. Comput
, 1993
"... We show how to efficiently construct a small probability space on n binary random variables such that for every subset, its parity is either zero or one with "almost" equal probability. They are called fflbiased random variables. The number of random bits needed to generate the random var ..."
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Cited by 276 (13 self)
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We show how to efficiently construct a small probability space on n binary random variables such that for every subset, its parity is either zero or one with "almost" equal probability. They are called fflbiased random variables. The number of random bits needed to generate the random
Crossover in Probability Spaces
"... This paper proposes a new crossover operator for searching over discrete probability spaces. The design of the operator is considered in the light of recent theoretical insights into genetic search provided by forma analysis. A nontrivial test problem in enforcing coherency of probability estimates ..."
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This paper proposes a new crossover operator for searching over discrete probability spaces. The design of the operator is considered in the light of recent theoretical insights into genetic search provided by forma analysis. A nontrivial test problem in enforcing coherency of probability
Gradient flows in metric spaces and in the space of probability measures
 LECTURES IN MATHEMATICS ETH ZÜRICH, BIRKHÄUSER VERLAG
, 2005
"... ..."
Duality and perfect probability spaces
 PROC. AMER. MATH. SOC
, 1996
"... Given probability spaces (Xi, Ai, Pi),i = 1, 2, let M(P1, P2) denote the set of all probabilities on the product space with marginals P1 and P2 and let h be a measurable function on (X1 × X2, A1 ⊗A2). Continuous versions of linear programming stemming from the works of Monge (1781) and Kantorovich ..."
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Cited by 3 (1 self)
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Given probability spaces (Xi, Ai, Pi),i = 1, 2, let M(P1, P2) denote the set of all probabilities on the product space with marginals P1 and P2 and let h be a measurable function on (X1 × X2, A1 ⊗A2). Continuous versions of linear programming stemming from the works of Monge (1781
GRAPH W∗PROBABILITY SPACES
, 2005
"... In this paper, we constructe a W ∗probability space (W ∗ (G), E) with amalgamation over a von Neumann algebra DG, where W ∗ (G) is a graph W ∗algebra induced by the countable directed graph G. In this structure, we compute the DGvalued moments and cumulants of arbitrary random variables, by usin ..."
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In this paper, we constructe a W ∗probability space (W ∗ (G), E) with amalgamation over a von Neumann algebra DG, where W ∗ (G) is a graph W ∗algebra induced by the countable directed graph G. In this structure, we compute the DGvalued moments and cumulants of arbitrary random variables
Conditional Probability Spaces
, 2007
"... Improper priors are used frequently, but often formally and without reference to a sound theoretical basis. The present paper demonstrates that Kolmogorov’s (1933) formulation of probability theory admits a minimal generalization which includes improper priors and a general Bayes theorem. The result ..."
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. The resulting theory is closely related to the theory of conditional probability spaces formulated by Renyi (1970), but the initial axioms and the motivation differ. The formulation includes Bayesian and conventional statistics as extreme cases, and suggests that intermediate cases can be considered. KEY WORDS
Geometry of probability spaces
 Constr. Approx
"... Partial differential equations and the Laplacian operator on domains in Euclidean spaces have played a central role in understanding natural phenomena. However this avenue has been limited in many areas where calculus is obstructed as in singular spaces, and function spaces of functions on a space X ..."
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Cited by 22 (1 self)
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Partial differential equations and the Laplacian operator on domains in Euclidean spaces have played a central role in understanding natural phenomena. However this avenue has been limited in many areas where calculus is obstructed as in singular spaces, and function spaces of functions on a space
Informative geometry of probability spaces
 Exposition. Math
, 1986
"... governmental purposes notwithstanding any copyright notation heron. ..."
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Cited by 9 (0 self)
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governmental purposes notwithstanding any copyright notation heron.
Why saturated probability spaces are necessary
, 2009
"... An atomless probability space (Ω, A,P) is said to have the saturation property for a probability measure μ on a product of Polish spaces X × Y if for every random element f of X whose law is margX(μ), there is a random element g of Y such that the law of (f, g) is μ. (Ω, A,P) is said to be saturated ..."
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Cited by 6 (2 self)
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An atomless probability space (Ω, A,P) is said to have the saturation property for a probability measure μ on a product of Polish spaces X × Y if for every random element f of X whose law is margX(μ), there is a random element g of Y such that the law of (f, g) is μ. (Ω, A,P) is said
Results 1  10
of
15,443