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11#
發(fā)表于 2025-3-23 11:38:11 | 只看該作者
12#
發(fā)表于 2025-3-23 14:09:26 | 只看該作者
The Chernoff Boundigh probability. When this is the case, we say that . is .. In this book, we will see a number of tools for proving that a random variable is concentrated, including Talagrand’s Inequality and Azuma’s Inequality. In this chapter, we begin with the simplest such tool, the Chernoff Bound.
13#
發(fā)表于 2025-3-23 20:00:53 | 只看該作者
14#
發(fā)表于 2025-3-23 22:30:50 | 只看該作者
Total Colouring Revisitedct with it. We then obtained a total colouring by modifying the edge colouring so as to eliminate the conflicts. In this chapter, we take the opposite approach, first choosing a vertex colouring and then choosing an edge colouring which does not conflict . with the vertex colouring, thereby obtaining a total colouring.
15#
發(fā)表于 2025-3-24 06:25:52 | 只看該作者
Talagrand’s Inequality and Colouring Sparse Graphs close to its expected value with high probability. Such tools are extremely valuable to users of the probabilistic method as they allow us to show that with high probability, a random experiment behaves approximately as we “expect” it to.
16#
發(fā)表于 2025-3-24 10:31:22 | 只看該作者
17#
發(fā)表于 2025-3-24 11:36:04 | 只看該作者
https://doi.org/10.1007/978-3-319-90584-6ex set has chromatic number 3. In other words, ... is strongly 3-colourable. Strongly .-colourable graphs are of interest partially because of their relationship to this problem, and also because they have other applications (see for example, Exercise 8.1).
18#
發(fā)表于 2025-3-24 17:09:22 | 只看該作者
19#
發(fā)表于 2025-3-24 21:37:48 | 只看該作者
20#
發(fā)表于 2025-3-25 00:49:17 | 只看該作者
https://doi.org/10.1007/978-3-476-03780-0otion of what an event is, which corresponds to this word’s use in everyday language. Formally, an . is a subset A of .. For example, we identify the event that the die roll is odd with the subset ({1, 3, 5}). Similarly, the event that the coin landed the same way up every time is the set ({.}).
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