粗野的整个
发表于 2025-3-21 19:11:47
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circumvent
发表于 2025-3-22 00:18:51
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minimal
发表于 2025-3-22 02:37:12
Convergence of Random Variables,is is called . A random variable is of course a function (.:Ω → . for an abstract space .), and thus we have the same notion: a sequence . → . . lim. .(.), for all .. This natural definition is surprisingly useless in probability. The next example gives an indication why.
encomiast
发表于 2025-3-22 06:20:00
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烧瓶
发表于 2025-3-22 11:56:47
https://doi.org/10.1007/978-3-642-51431-9Brownian motion; Martingal; Martingale; Martingales; Random variable; central limit theorem; conditional p
健谈
发表于 2025-3-22 15:30:51
Springer-Verlag Berlin Heidelberg 2000
STAT
发表于 2025-3-22 17:34:46
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diabetes
发表于 2025-3-22 22:45:36
Conditional Probability and Independence,Let . and . be two events defined on a probability space. Let .(.) denote the number of times . occurs divided by .. Intuitively, as n gets large, .(.) should be close to .. Informally, we should have ..
案发地点
发表于 2025-3-23 03:39:28
Probabilities on a Countable Space,For Chapter 4, we assume . is countable, and we take . = 2. (the class of all subsets of .).
Factual
发表于 2025-3-23 08:16:25
Construction of a Probability Measure,Here we no longer assume . is countable. We assume given . and a .-algebra . ⊂ 2.. (., .) is called a . We want to construct probability measures on . When . is finite or countable we have already-seen this is simple to do. When . is uncountable, the same technique does not work; indeed, a “typical” probability . will have .({.}) = 0 for all .