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The Strong Law of Large Numbers
1994Abstract 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
199615.1 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, 1976Nagaev, S. V., Volodin, N. A.
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On Uniform Versions of the Strong Law of Large Numbers
Mathematische Nachrichten, 1989For non-negative bounded weights \(p_ 1\), \(p_ 2,..\).
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On The Strong Law of Large Numbers
1992Let 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, 1978Volodin, N. A., Nagaev, S. V.
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A strong law of large numbers for non-additive probabilities
International Journal of Approximate Reasoning, 2013Zengjing Chen, Panyu Wu, Baoming Li
exaly
A Strong Law of Large Numbers for Random Compact Sets
Annals of Probability, 1975Zvi 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, 2018Qunying Wu, Yuanying Jiang
exaly

