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- The following topics will be covered in this lecture:
- Multiple random variables
- CDF and PDF of multiple variables
- Marginals
- The ensemble matrix
- The expected value
- The ensemble mean

We will now introduce the basic tools of

**statistics and probability theory for multivariate analysis**;- we will be studying the relations between
**\( N_x>1 \) total RVs that will often covary together in their conditional probabilities**.

- we will be studying the relations between
Some notions like the expected / center of mass will translate directly over linear combinations of RVs.

Recall, for RVs \( X,Y \) and a constant scalars \( a,b \) we have \[ \mathbb{E}\left[ a X + b Y\right] = a \mathbb{E}\left[X\right] + b \mathbb{E}\left[Y\right] \] by the linearity of the expectation.

Recall the vector notation \( \pmb{x} \in \mathbb{R}^{N_x} \), matrix notation \( \mathbf{A} \in \mathbb{R}^{N_x \times N_x} \), and matrix-slice notation \( \mathbf{A}^j \in \mathbb{R}^{N_x} \) where

\[ \begin{align} \pmb{x} := \begin{pmatrix} x_1 \\ \vdots \\ x_{N_x} \end{pmatrix} & & \mathbf{A} := \begin{pmatrix} a_{1,1} & \cdots & a_{1, N_x} \\ \vdots & \ddots & \vdots \\ a_{N_x,1} & \cdots & a_{N_x, N_x} \end{pmatrix} & & \mathbf{A}^j := \begin{pmatrix} a_{1,j} \\ \vdots \\ a_{N_x, j} \end{pmatrix} \end{align} \]

The linearity of the expectation then extends to random vectors \( \pmb{x}, \pmb{y} \) and constant matrices \( \mathbf{A},\mathbf{B} \), we can write \[ \mathbb{E}\left[\mathbf{A}\pmb{x} + \mathbf{B}\pmb{y} \right] = \mathbf{A}\mathbb{E}\left[ \pmb{x}\right] + \mathbf{B}\mathbb{E}\left[\pmb{y}\right]. \]

While the concept of the

**center of mass**has a**direct generalization to vectors**, we will need to make some additional considerations when we measure the spread of RVs and how they relate to others.The extension of the

**second centered moment**has the generalization to the**rotational inertia tensor**.

We will begin our consideration in \( N_x=2 \) dimensions, as all properties described in the following will extend (with minor modifications) to arbitrarily large but finite \( N_x \).

Let the random vector \( \pmb{X} \) be defined as

\[ \begin{align} \pmb{X} = \begin{pmatrix} X_1 \\ X_2 \end{pmatrix} \end{align} \] where each of the above components \( X_i \) is a RV.

We can define the

**cumulative distribution function**in a similar way to the definition in one variable.Let \( x_1,x_2 \) be

**two fixed real values forming a constant vector**as\[ \pmb{x} = \begin{pmatrix} x_1 \\ x_2\end{pmatrix}. \]

Define the comparison operator between two vectors \( \pmb{y}, \pmb{x} \) as

\[ \pmb{y} \leq \pmb{x} \Leftrightarrow y_i \leq x_i \text{ for each and every }i \]

Multivariate cumulative distribution function

Thecumulative distribution function\( P \), describing the probability of realizations of \( \pmb{X} \), is defined \[ P(\pmb{x}) = \mathcal{P}(\pmb{X}\leq \pmb{x} ) = \mathcal{P}(X_i \leq x_i \quad \forall i=1,\cdots,N_x). \]

Recall that the CDF

\[ \begin{align} P:\mathbb{R}^2 & \rightarrow [0,1] \\ \pmb{x} & \rightarrow \mathcal{P}(\pmb{X}\leq \pmb{x}) \end{align} \] is a function of the variables \( (x_1,x_2) \).

Suppose then that \( P \) has continuous second partial derivatives in \( \partial_{x_1} \partial_{x_2}P = \partial_{x_2}\partial_{x_1}P \);

- this implies we can arbitrarily exchange the order of differentiation or integration.

Multivariate probability density function

Let \( P\in \mathcal{C}^2\left(\mathbb{R}^2\right) \) , then theprobability density function\( p \) is defined as \[ \begin{align} p:\mathbb{R}^2 & \rightarrow \mathbb{R}\\ \pmb{x} &\rightarrow \partial_{x_1}\partial_{x_2}P(\pmb{x}) \end{align} \]

In the above definition, we have constructed the density function in the same way as in one variable;

- specifically, in the case where \( p \) itself is differentiable, we have defined the CDF as the anti-derivative of the density:

\[ \begin{align} P(\pmb{x}) = \int_{-\infty}^{x_1} \int_{-\infty}^{x_2} p(s_1, s_2) \mathrm{d}s_1 \mathrm{d}s_2. \end{align} \]

Recall that the relationship between the CDF and the PDF as

\[ \begin{align} P(\pmb{x}) = \int_{-\infty}^{x_1} \int_{-\infty}^{x_2} p(s_1, s_2) \mathrm{d}s_1 \mathrm{d}s_2; \end{align} \]

this implies that

\[ \begin{align} P(- \infty < \pmb{X} < \infty) = \int_{-\infty}^{\infty} \int_{-\infty}^{\infty} p(s_1, s_2) \mathrm{d}s_1 \mathrm{d}s_2 = 1 \end{align} \]

If we define this over \( N_x\geq 2 \) variables, all of the above extends identically when \( P \) has derivatives defined in arbitrary arrangements of the \( N_x \)-th partial derivatives in each \( \partial_{x_i} \).

Specifically, this requires that, for all permutations \( \psi:(1, \cdots, N_x) \rightarrow (\psi(1), \cdots ,\psi(N_x)) \),

\[ \begin{align} \partial_{x_1} \cdots \partial_{x_{N_x}}P = \partial_{x_{\psi(1)}} \cdots \partial_{x_{\psi(N_x)}}P \end{align} \]

- We then construct \( p \) as the \( N_x \)-th partial derivative of \( P \) in all univariate partial derivatives.

Using the integral relationship, we can similarly show

\[ \begin{align} P(\pmb{x}) = \int_{-\infty}^{x_1} \cdots \int_{-\infty}^{x_{N_x}} p(s_1, \cdots, s_{N_x}) \mathrm{d}s_1\cdots \mathrm{d}s_{N_x}. \end{align} \]

- Particularly, we will again view the density function like the curve of the univariate case, but for two variables we see this as a
**surface above the \( x_1,x_2 \) plane**.

Courtesy of: F.M. Dekking, et al. *A Modern Introduction to Probability and Statistics*. Springer Science & Business Media, 2005.

- To the right, we see the multivariate Gaussian bell surface that defines the multivariate normal distribution in two variables.
- In one variable, the probability \( \mathcal{P}(X_1 \leq x_1) \) was associated to the area under the curve, computed by the integral of the density.