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index.html

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<article>
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<h1>Hello, I'm Robin.</h1>
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<p>
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A little bit of self intro, I'm a <del>freshman</del> sophomore at the NYU studying Computer Science and Mathematics.
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This site serves as a platform for me to document my thoughts, projects, and anything else I find
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worth sharing. I share random ideas and stuff I created about school, life, etc. (Not any type of
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category that I want to fall into, journal, diary, because it would feel kind of restrictive on
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'how'/'what' should I write about anything.)
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A little bit of self intro, I'm a <del>freshman</del> sophomore at the NYU studying Mathematics and
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Computer Science. (Planning on getting a minor in linguistics) This site serves as a platform for me
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to document my thoughts, projects, and anything else I find worth sharing. I share random ideas and
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stuff I created about school, life, etc. (Not any type of category that I want to fall into, journal,
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diary, because it would feel kind of restrictive on 'how'/'what' should I write about anything.)
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</p>
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<h2>What You Might Find Here</h2>
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<h2>Tech Stack</h2>
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<p>
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This site is built with vanilla HTML, CSS, and JavaScript. I'm trying to use minimal dependencies to
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stay
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lightweight, the ones being used including:
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stay lightweight, the ones being used including:
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</p>
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<ul>
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<li><code>js-yaml</code> - parse front-matter in notes</li>
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<h2>Future Plans</h2>
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<p>
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I plan to add more features and improve the design over time. Some ideas include:
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I plan to add more features and improve the design over time. Email me if you have suggestions!
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Currently, some ideas include:
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</p>
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<ul>
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<li><del>Improving the comment system</del> (Done)</li>
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<li>Converting all my course notes into markdown</li>
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<li>Organizing stuff I wrote during the summer into blog posts</li>
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<li><u>Converting all my course notes into markdown</u> (Done partial, in progress)</li>
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<li><u>Organizing stuff I wrote during the summer into blog posts</u> (In progress)</li>
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<li>Adding a search feature for easier navigation</li>
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<li><u>Enhancing the overall design and responsiveness</u> (In progress)</li>
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<li><del>Enhance the overall design and responsiveness</del> (Done)</li>
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<li><u>Supporting more Markdown features</u> (In progress for parsing in general)</li>
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<li><u>Notifying users of new posts or updates</u> (In progress)</li>
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<li>Add a section for ideas pop up in my mind</li>
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<li>Notifying users of new posts or updates</li>
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</article>
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</main>
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notes/courses/MATH-UA-333/04-05-drv.md

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---
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## Expectation
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For a discrete random variable $X$ with p.m.f. $p_X(t)=P[X=t]$,
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$$
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\E[X] = \sum_{t} t p_X(t), \quad \E[f(X)] = \sum_t f(t) p_X(t),
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$$
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**Interpretations**
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- Weighted average over the values of $X$: $\sum_t t P[X=t]$.
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- Weighted average over outcomes $\omega$ in the sample space $S$: $\sum_{\omega\in S} X(\omega) P(\{\omega\})$.
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> Examples:
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>
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> - Fair die $X\in\{1,\dots,6\}$: $\E[X]=3.5$.
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> - Heads in three fair flips $Y\in\{0,1,2,3\}$: $\E[Y]=1.5$.
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> - Indicator ${\mathbf{1}}_A$: $\E[{\mathbf{1}}_A]=P(A)$.
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## Linearity of expectation
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For random variables $X,Y$ and scalar $a\in\mathbb{R}$,
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$$
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\E[aX+Y] = a \E[X] + \E[Y].
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$$
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No independence required.
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**Counting-by-indicators trick**
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If $X=\sum_{i=1}^n {\mathbf{1}}_{A_i}$ counts how many events $A_i$ occur, then
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$$
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\E[X] = \sum_{i=1}^n P(A_i).
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$$
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> Example: number of fixed points in a random permutation - $\E[X]=1$.
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## Variance
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Variance measures spread around the mean:
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$$
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\operatorname{Var}(X) = \E[(X-\E X)^2] = \E[X^2] - (\E X)^2.
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$$
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Useful identities:
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- For constants $a,b$: $\operatorname{Var}(aX+b)=a^2 \operatorname{Var}(X)$.
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- For an indicator: $\operatorname{Var}({\mathbf{1}}_A)=P(A)\big(1-P(A)\big)$.
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- If $X=\sum_{i=1}^n {\mathbf{1}}_{A_i}$ with independent indicators, then
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$$
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\operatorname{Var}(X)=\sum_{i=1}^n P(A_i)\big(1-P(A_i)\big).
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$$
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$$
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\operatorname{Var}(X)=\sum_{i=1}^n P(A_i)\big(1-P(A_i)\big).
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$$
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## Inclusion–Exclusion via indicators
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Using ${\mathbf{1}}_{A^c} = 1 - {\mathbf{1}}_A$ and expanding
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${\mathbf{1}}_{\cup_i E_i} = 1 - \prod_{i=1}^n (1-{\mathbf{1}}_{E_i})$, then taking expectations yields
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Using ${\mathbf{1}}_{A^c} = 1 - {\mathbf{1}}_A$ and expanding ${\mathbf{1}}_{\cup_i E_i} = 1 - \prod_{i=1}^n (1-{\mathbf{1}}_{E_i})$, then taking expectations yields
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$$
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P\Big( \bigcup_{i=1}^n\ E_i \Big)
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P\Big( \bigcup_{i=1}^n\ {\E}_i \Big)
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= \sum_{k=1}^n (-1)^{k-1}
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\sum_{1 \leq i_1 < \cdots < i_k \leq n}
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P(\E_{i_1} \cap \cdots \cap \E_{i_k}).
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### Binomial
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Let $X$ be the total number of successes in $n$ independent Bernoulli$(p)$ trials. Then $X\sim \operatorname{Bin}(n,p)$ and can be written $X=X_1+\cdots+X_n$ with $X_i\sim\operatorname{Ber}(p)$. The pmf is
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Let $X$ be the total number of successes in $n$ independent Bernoulli$(p)$ trials. Then $X\sim \operatorname{Bin}(n,p)$ and can be written $X=X_1+\cdots+X_n$ with $X_i\sim\operatorname{Ber}(p)$. The pmf is
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$$
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P(X=k) = {n\choose k} p^k (1-p)^{ n-k}.
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$$

posts/metadata/entries.json

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"author": "R",
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"location": "Futian district, Shenzhen, Guangdong, China",
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"tags": ["cs"],
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"slug": "014-server-dev"
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"slug": "014-server"
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}
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,
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{
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"author": "R",
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"location": "Warren Weaver Hall, NYU, New York, NY",
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"tags": ["notes", "cs"],
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"slug": "015-seminar-anubis"
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"slug": "020-seminar-anubis"
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}
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]

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