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The Strong Law of Large Numbers

1994
Abstract This chapter focuses largely on methods of proof of the strong law, building on the fundamental convergence lemma. It covers Kolmogorov's three‐series theorem, strong laws for martingales, and random weighting. Then a range of strong laws are proved for mixingales and for near‐epoch dependent and mixing processes.
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The Strong Law of Large Numbers

1996
15.1 This section gives some fundamental definitions in the theory of probability, such as the definitions of a probability space and a random variable.
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On the Strong Law of Large Numbers

Theory of Probability & Its Applications, 1976
Nagaev, S. V., Volodin, N. A.
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On Uniform Versions of the Strong Law of Large Numbers

Mathematische Nachrichten, 1989
For non-negative bounded weights \(p_ 1\), \(p_ 2,..\).
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On The Strong Law of Large Numbers

1992
Let be a sequence of independent random variables with zero expectations E(x n ). Following Cantelli and Khinchin we say that (1) satisfies the strong law of large numbers (SLLN) if the probability of convergence to zero of the means is equal to 1.
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A Remark on the Strong Law of Large Numbers

Theory of Probability & Its Applications, 1978
Volodin, N. A., Nagaev, S. V.
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A Strong Law of Large Numbers

Econometric Theory, 1996
de Jong, RM, Kemp, GCR, Xu Zheng, J
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A strong law of large numbers for non-additive probabilities

International Journal of Approximate Reasoning, 2013
Zengjing Chen, Panyu Wu, Baoming Li
exaly  

A Strong Law of Large Numbers for Random Compact Sets

Annals of Probability, 1975
Zvi Artstein, Richard A Vitale
exaly  

Strong law of large numbers and Chover's law of the iterated logarithm under sub-linear expectations

Journal of Mathematical Analysis and Applications, 2018
Qunying Wu, Yuanying Jiang
exaly  

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