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Calculus-based Probability

This is content covered in STAT410 and STAT700 at UMD.

Basics

Axioms of Probability

\(\displaystyle 0 \leq P(E) \leq 1\)
\(\displaystyle P(S) = 1\) where \(\displaystyle S\) is your sample space
For mutually exclusive events \(\displaystyle E_1, E_2, ...\), \(\displaystyle P\left(\bigcup_i^\infty E_i\right) = \sum_i^\infty P(E_i)\)

Monotonicity

For all events \(\displaystyle A\) and \(\displaystyle B\), \(\displaystyle A \subset B \implies P(A) \leq P(B)\)

Proof

PMF, PDF, CDF

For discrete distributions, we call \(\displaystyle p_{X}(x)=P(X=x)\) the probability mass function (PMF).
For continuous distributions, we have the probability density function (PDF) \(\displaystyle f(x)\).
The comulative distribution function (CDF) is \(\displaystyle F(x) = P(X \leq x)\).
The CDF is the prefix sum of the PMF or the integral of the PDF. Likewise, the PDF is the derivative of the CDF.

Expectation, Variance, and Moments

Some definitions and properties.

Definitions

Let \(\displaystyle X \sim D\) for some distribution \(\displaystyle D\). Let \(\displaystyle S\) be the support or domain of your distribution.

\(\displaystyle E(X) = \sum_S xp(x)\) or \(\displaystyle \int_S xp(x)dx\)
\(\displaystyle Var(X) = E[(X-E(X))^2] = E(X^2) - (E(X))^2\)

Total Expection

\(\displaystyle E_{X}(X) = E_{Y}(E_{X|Y}(X|Y))\)
Dr. Xu refers to this as the smooth property.

Proof

\(\displaystyle \begin{aligned} E(X) &= \int_S x p(x)dx \\ &= \int_x x \int_y p(x,y)dy dx \\ &= \int_x x \int_y p(x|y)p(y)dy dx \\ &= \int_y\int_x x p(x|y)dxp(y)dy \end{aligned} \)

Total Variance

\(\displaystyle Var(Y) = E(Var(Y|X)) + Var(E(Y | X))\)
This one is not used as often on tests as total expectation

Proof

\(\displaystyle \begin{aligned} Var(Y) &= E(Y^2) - E(Y)^2 \\ &= E(E(Y^2|X)) - E(E(Y|X))^2\\ &= E(Var(Y|X) + E(Y|X)^2) - E(E(Y|X))^2\\ &= E((Var(Y|X)) + E(E(Y|X)^2) - E(E(Y|X))^2\\ &= E((Var(Y|X)) + Var(E(Y|X))\\ \end{aligned} \)

Sample Mean and Variance

The sample mean is \(\displaystyle \bar{X} = \frac{1}{n}\sum_{i=1}^{n}X_i\).
The unbiased sample variance is \(\displaystyle S^2 = \frac{1}{n-1}\sum_{i=1}^{n}(X_i - \bar{X})^2\).

Student's Theorem

Let \(\displaystyle X_1,...,X_n\) be from \(\displaystyle N(\mu, \sigma^2)\).
Then the following results about the sample mean \(\displaystyle \bar{X}\) and the unbiased sample variance \(\displaystyle S^2\) hold:

\(\displaystyle \bar{X}\) and \(\displaystyle S^2\) are independent
\(\displaystyle \bar{X} \sim N(\mu, \sigma^2 / n)\)
\(\displaystyle (n-1)S^2 / \sigma^2 \sim \chi^2(n-1)\)

Moments

\(\displaystyle E(X^n)\) the n'th moment
\(\displaystyle E((X-\mu)^n)\) the n'th central moment
\(\displaystyle E(((X-\mu) / \sigma)^n)\) the n'th standardized moment

Expectation is the first moment and variance is the second central moment.
Additionally, skew is the third standardized moment and kurtosis is the fourth standardized moment.

To compute moments, we can use a moment generating function (MGF): \(\displaystyle M_X(t) = E(e^{tX})\) With the MGF, we can get any order moments by taking n derivatives and setting \(t=0\).

Moments and Moment Generating Functions

Definitions

We call \(\displaystyle E(X^i)\) the i'th moment of \(\displaystyle X\).
We call \(\displaystyle E(|X - E(X)|^i)\) the i'th central moment of \(\displaystyle X\).
Therefore the mean is the first moment and the variance is the second central moment.

Moment Generating Functions

\(\displaystyle E(e^{tX})\)
We call this the moment generating function (mgf).
We can differentiate it with respect to \(\displaystyle t\) and set \(\displaystyle t=0\) to get the higher moments.

Notes

The mgf, if it exists, uniquely defines the distribution.
The mgf of \(\displaystyle X+Y\) is \(\displaystyle E(e^{t(X+Y)})=E(e^{t(X)})E(e^{t(Y)})\)

Characteristic function

Convergence

There are 4 common types of convergence.

Almost Surely

\(\displaystyle P(\lim X_i = X) = 1\)

In Probability

For all \(\displaystyle \epsilon \gt 0\)
\(\displaystyle \lim P(|X_i - X| \geq \epsilon) = 0\)

Implies Convergence in distribution

In Distribution

Pointwise convergence of the cdf
A sequence of random variables \(\displaystyle X_1,...\) converges to \(\displaystyle X\) in probability if for all \(\displaystyle x \in S\),
\(\displaystyle \lim_{i \rightarrow \infty} F_i(x) = F(x)\)

Equivalent to convergence in probability if it converges to a degenerate distribution (i.e. a number)

In Mean Squared

\(\displaystyle \lim_{i \rightarrow \infty} E(|X_i-X|^2)=0\)

Delta Method

Suppose \(\displaystyle \sqrt{n}(X_n - \theta) \xrightarrow{D} N(0, \sigma^2)\).
Let \(\displaystyle g\) be a function such that \(\displaystyle g'\) exists and \(\displaystyle g'(\theta) \neq 0\)
Then \(\displaystyle \sqrt{n}(g(X_n) - g(\theta)) \xrightarrow{D} N(0, \sigma^2 g'(\theta)^2)\)

Multivariate:
\(\displaystyle \sqrt{n}(B - \beta) \xrightarrow{D} N(0, \Sigma) \implies \sqrt{n}(h(B)-h(\beta)) \xrightarrow{D} N(0, h'(\theta)^T \Sigma h'(\theta))\)

Notes

You can think of this like the Mean Value theorem for random variables.
- \(\displaystyle (g(X_n) - g(\theta)) \approx g'(\theta)(X_n - \theta)\)

Order Statistics

Inequalities and Limit Theorems

Markov's Inequality

Let \(\displaystyle X\) be a non-negative random variable.
Then \(\displaystyle P(X \geq a) \leq \frac{E(X)}{a}\)

Proof

\(\displaystyle \begin{aligned} E(X) &= \int_{0}^{\infty}xf(x)dx \\ &= \int_{0}^{a}xf(x)dx + \int_{a}^{\infty}xf(x)dx\\ &\geq \int_{a}^{\infty}xf(x)dx\\ &\geq \int_{a}^{\infty}af(x)dx\\ &=a \int_{a}^{\infty}f(x)dx\\ &=a * P(X \geq a)\\ \implies& P(X \geq a) \leq \frac{E(X)}{a} \end{aligned} \)

Chebyshev's Inequality

\(\displaystyle P(|X - \mu| \geq k \sigma) \leq \frac{1}{k^2}\)
\(\displaystyle P(|X - \mu| \geq k) \leq \frac{\sigma^2}{k^2}\)

Proof

Apply Markov's inequality:
Let \(\displaystyle Y = |X - \mu|\)
Then \(\displaystyle P(|X - \mu| \geq k) = P(Y \geq k) = = P(Y^2 \geq k^2) \leq \frac{E(Y^2)}{k^2} = \frac{E((X - \mu)^2)}{k^2}\)

Usually used to prove convergence in probability

Central Limit Theorem

Very very important. Never forget this.
For any distribution, the sample mean converges in distribution to normal.
Let \(\displaystyle \mu = E(x)\) and \(\displaystyle \sigma^2 = Var(x)\)
Different ways of saying the same thing:

\(\displaystyle \sqrt{n}(\bar{x} - \mu) \sim N(0, \sigma^2)\)
\(\displaystyle \frac{\sqrt{n}}{\sigma}(\bar{x} - \mu) \sim N(0, 1)\)
\(\displaystyle \bar{x} \sim N(\mu, \sigma^2/n)\)

Law of Large Numbers

The sample mean converges to the population mean in probability.
For all \(\displaystyle \epsilon \gt 0\), \(\displaystyle \lim_{n \rightarrow \infty} P(|\bar{X}_n - E(X)| \geq \epsilon) = 0\)

Notes

The sample mean converges to the population mean almost surely.

Properties and Relationships between distributions

This is important for exams.

Poisson Distribution

If \(\displaystyle X_i \sim Poisson(\lambda_i)\) then \(\displaystyle \sum X_i \sim Poisson(\sum \lambda_i)\)

Normal Distribution

If \(\displaystyle X_1 \sim N(\mu_1, \sigma_1^2)\) and \(\displaystyle X_2 \sim N(\mu_2, \sigma_2^2)\) then \(\displaystyle \lambda_1 X_1 + \lambda_2 X_2 \sim N(\lambda_1 \mu_1 + \lambda_2 X_2, \lambda_1^2 \sigma_1^2 + \lambda_2^2 + \sigma_2^2)\) for any \(\displaystyle \lambda_1, \lambda_2 \in \mathbb{R}\)

Exponential Distribution

\(\displaystyle \operatorname{Exp}(\lambda)\) is equivalent to \(\displaystyle \Gamma(1, 1/\lambda)\)
- Note that some conventions flip the second parameter of gamma, so it would be \(\displaystyle \Gamma(1, \lambda)\)
If \(\displaystyle \epsilon_1, ..., \epsilon_n\) are exponential distributions then \(\displaystyle \min\{\epsilon_i\} \sim \exp(\sum \lambda_i)\)
Note that the maximum is not exponentially distributed
- However, if \(\displaystyle X_1, ..., X_n \sim \exp(1)\) then \(\displaystyle Z_n=n\exp(\max\{\epsilon_i\}) \rightarrow \exp(1)\)

Gamma Distribution

Note exponential distributions are also Gamma distrubitions

If \(\displaystyle X \sim \Gamma(k, \theta)\) then \(\displaystyle \lambda X \sim \Gamma(k, c\theta)\).
If \(\displaystyle X_1 \sim \Gamma(k_1, \theta)\) and \(\displaystyle X_2 \sim \Gamma(k_2, \theta)\) then \(\displaystyle X_2 + X_2 \sim \Gamma(k_1 + k_2, \theta)\).
If \(\displaystyle X_1 \sim \Gamma(\alpha, \theta)\) and \(\displaystyle X_2 \sim \Gamma(\beta, \theta)\), then \(\displaystyle \frac{X_1}{X_1 + X_2} \sim B(\alpha, \beta)\).

T-distribution

Ratio of standard normal and squared-root of Chi-sq distribution yields T-distribution.
- If \(\displaystyle Z \sim N(0,1)\) and \(\displaystyle V \sim \Chi^2(v)\) then \(\displaystyle \frac{Z}{\sqrt{V/v}} \sim \text{t-dist}(v)\)

Chi-Sq Distribution

The ratio of two normalized Chi-sq is an F-distributions
- If \(\displaystyle X \sim \chi^2_{d1}\) and \(\displaystyle Y \sim \chi^2_{d2}\) then \(\displaystyle \frac{X/d1}{Y/d2} \sim F(d1,d2)\)
If \(\displaystyle Z_1,...,Z_k \sim N(0,1)\) then \(\displaystyle Z_1^2 + ... + Z_k^2 \sim \Chi^2(k)\)
If \(\displaystyle X_i \sim \Chi^2(k_i)\) then \(\displaystyle X_1 + ... + X_n \sim \Chi^2(k_1 +...+ k_n)\)
\(\displaystyle \Chi^2(k)\) is equivalent to \(\displaystyle \Gamma(k/2, 2)\)

F Distribution

Too many to list. See Wikipedia: F-distribution.

Most important are Chi-sq and T distribution:

If \(\displaystyle X \sim \chi^2_{d1}\) and \(\displaystyle Y \sim \chi^2_{d2}\) then \(\displaystyle \frac{X/d1}{Y/d2} \sim F(d1,d2)\)
If \(\displaystyle X \sim t_{(n)}\) then \(\displaystyle X^2 \sim F(1, n)\) and \(\displaystyle X^{-2} \sim F(n, 1)\)

Textbooks

Sheldon Ross' A First Course in Probability
- This is a very good textbook that is standard across many universities. However, it only covers one semester of content.

The books below cover both introductory probability as well as statistics.