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10 November 2024

  • 14:3814:38, 10 November 2024 diff hist +1,093 N Assignment 3, Fall 2024Created page with "Held-Hou(1980) 讨论了当外部强迫的经向分布呈二次勒让德多项式,即<math>\dfrac{\Theta_E(\phi,z)}{\Theta_o}=1-\dfrac{2}{3}\Delta_H P_2(\sin \phi)+\Delta_v(\dfrac{z}{H}-\dfrac{1}{2})</math>的情况下,哈德莱环流内的风场、温度场、环流的空间范围等将怎样随纬度和外力强迫的强度而变化。 如果将外力强迫的空间分布改为<math>\dfrac{\Theta_E(\phi,z)}{\Theta_o}=1-\Delta_H(\sin^3 \phi - \dfrac{1}{4})+\Delta_v..." current
  • 14:3714:37, 10 November 2024 diff hist +59 General Circulation(Fall 2024)→‎Assignments current

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19 September 2024

  • 11:3211:32, 19 September 2024 diff hist +10,715 N 高级算法 (Fall 2024)/Conditional expectationsCreated page with "= Conditional Expectations = The '''conditional expectation''' of a random variable <math>Y</math> with respect to an event <math>\mathcal{E}</math> is defined by :<math> \mathbf{E}[Y\mid \mathcal{E}]=\sum_{y}y\Pr[Y=y\mid\mathcal{E}]. </math> In particular, if the event <math>\mathcal{E}</math> is <math>X=a</math>, the conditional expectation :<math> \mathbf{E}[Y\mid X=a] </math> defines a function :<math> f(a)=\mathbf{E}[Y\mid X=a]. </math> Thus, <math>\mathbf{E}[Y\mid..." current
  • 11:3111:31, 19 September 2024 diff hist +41,013 N 高级算法 (Fall 2024)/Concentration of measureCreated page with "=Chernoff Bound= Suppose that we have a fair coin. If we toss it once, then the outcome is completely unpredictable. But if we toss it, say for 1000 times, then the number of HEADs is very likely to be around 500. This phenomenon, as illustrated in the following figure, is called the '''concentration''' of measure. The Chernoff bound is an inequality that characterizes the concentration phenomenon for the sum of independent trials. File:Coinflip.png|border|450px|cent..." current
  • 11:3011:30, 19 September 2024 diff hist +229 高级算法 (Fall 2024)→‎Lecture Notes

18 September 2024

16 September 2024

  • 02:4502:45, 16 September 2024 diff hist +5,755 N 高级算法 (Fall 2024)/Basic deviation inequalitiesCreated page with "=Markov's Inequality= One of the most natural information about a random variable is its expectation, which is the first moment of the random variable. Markov's inequality draws a tail bound for a random variable from its expectation. {{Theorem |Theorem (Markov's Inequality)| :Let <math>X</math> be a random variable assuming only nonnegative values. Then, for all <math>t>0</math>, ::<math>\begin{align} \Pr[X\ge t]\le \frac{\mathbf{E}[X]}{t}. \end{align}</math> }} {{Proo..." current
  • 02:4402:44, 16 September 2024 diff hist +15,812 N 高级算法 (Fall 2024)/Limited independenceCreated page with "= <math>k</math>-wise independence = Recall the definition of independence between events: {{Theorem |Definition (Independent events)| :Events <math>\mathcal{E}_1, \mathcal{E}_2, \ldots, \mathcal{E}_n</math> are '''mutually independent''' if, for any subset <math>I\subseteq\{1,2,\ldots,n\}</math>, ::<math>\begin{align} \Pr\left[\bigwedge_{i\in I}\mathcal{E}_i\right] &= \prod_{i\in I}\Pr[\mathcal{E}_i]. \end{align}</math> }} Similarly, we can define independence between..." current
  • 02:4402:44, 16 September 2024 diff hist +49,238 N 高级算法 (Fall 2024)/Hashing and SketchingCreated page with "=Balls into Bins= The following is the so-called balls into bins model. Consider throwing <math>m</math> balls into <math>n</math> bins uniformly and independently at random. This is equivalent to a random mapping <math>f:[m]\to[n]</math>. Needless to say, random mapping is an important random model and may have many applications in Computer Science, e.g. hashing. We are concerned with the following three questions regarding the balls into bins model: * birthday problem..."
  • 02:4402:44, 16 September 2024 diff hist +304 高级算法 (Fall 2024)→‎Lecture Notes

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