Statistics

Distribution

For each distribution, we try to figure out:

• What is its support?
• What is the p.m.f.?
• What are the parameters?
• What is $E(X)$? What is $Var(X)$ and s.d.?
• Why is it important?

Discrete vs. Continuous Distributions

	Discrete	Continuous
Expected Value	$E(x)=\sum xf(x)$	$E(x)={\int }_{-\infty }^{\infty }x(f(x))dx$
Variance	$Var(x)=E(x^2)-(E(x){)}^2$	$Var(x)={\int }_{-\infty }^{\infty }{x}^{2}f(x)dx-[{\int }_{-\infty }^{\infty }x(f(x){]}^2$
pmf to cdf	$F(x)={\sum }_{y:y\le x}f(y)$	$F(x)={\int }_{-\infty }^{x}f(y)dy$
cdf to pdf	$f(x)=F(x)-F(x-1)$	$\frac{dF}{dx}=f(x)$

Distributions

• Bernoulli Distribution
• Binomial Distribution
• Beta Distribution
• Geometric Distribution
• Negative Binomial Distribution
• Normal Distribution
• Hypergeometric Distribution
• Poisson Distribution
• Multinomial Distribution
• Cauchy-Lorentz Distribution

Concepts

• Standard Deviation
• Variance

Measures of dispersion and symmetry

1. Range = max - min
2. IQR (inter-quartile range) = $Q_3-Q_1$
3. Variance (standard dev), $s^2=\frac{1}{n-1}\sum (x_i-\overline{x}{)}^2$
4. Skewness - measures bias towards left or right side
5. Kurtosis - measure of normality

1D Distribution

I was having trouble understanding what a 1D Distribution was, since I needed to use that to calculate the Wasserstein Metric. Um, I think it’s just with one variable.

2D Distribution

This is where we talk about multivariate distributions. https://en.wikipedia.org/wiki/Multivariate_normal_distribution