Notation

🏷️chap_notation

The notation used throughout this book is summarized below.

Numbers

$x$: A scalar
$\mathbf{x}$: A vector
$\mathbf{X}$: A matrix
$\mathsf{X}$: A tensor
$\mathbf{I}$: An identity matrix
$x_i$, $[\mathbf{x}]_i$: The $i^\mathrm{th}$ element of vector $\mathbf{x}$
$x_{ij}$, $x_{i,j}$,$[\mathbf{X}]{ij}$, $[\mathbf{X}]{i,j}$: The element of matrix $\mathbf{X}$ at row $i$ and column $j$

$\mathcal{X}$: A set
$\mathbb{Z}$: The set of integers
$\mathbb{Z}^+$: The set of positive integers
$\mathbb{R}$: The set of real numbers
$\mathbb{R}^n$: The set of $n$-dimensional vectors of real numbers
$\mathbb{R}^{a\times b}$: The set of matrices of real numbers with $a$ rows and $b$ columns
$|\mathcal{X}|$: Cardinality (number of elements) of set $\mathcal{X}$
$\mathcal{A}\cup\mathcal{B}$: Union of sets $\mathcal{A}$ and $\mathcal{B}$
$\mathcal{A}\cap\mathcal{B}$: Intersection of sets $\mathcal{A}$ and $\mathcal{B}$
$\mathcal{A}\setminus\mathcal{B}$: Subtraction of set $\mathcal{B}$ from set $\mathcal{A}$

$f(\cdot)$: A function
$\log(\cdot)$: The natural logarithm
$\exp(\cdot)$: The exponential function
$\mathbf{1}_\mathcal{X}$: The indicator function
$\mathbf{(\cdot)}^\top$: Transpose of a vector or a matrix
$\mathbf{X}^{-1}$: Inverse of matrix $\mathbf{X}$
$\odot$: Hadamard (elementwise) product
$[\cdot, \cdot]$: Concatenation
$\lvert \mathcal{X} \rvert$: Cardinality of set $\mathcal{X}$
$|\cdot|_p$: $L_p$ norm
$|\cdot|$: $L_2$ norm
$\langle \mathbf{x}, \mathbf{y} \rangle$: Dot product of vectors $\mathbf{x}$ and $\mathbf{y}$
$\sum$: Series addition
$\prod$: Series multiplication
$\stackrel{\mathrm{def}}{=}$: Definition

$\frac{dy}{dx}$: Derivative of $y$ with respect to $x$
$\frac{\partial y}{\partial x}$: Partial derivative of $y$ with respect to $x$
$\nabla_{\mathbf{x}} y$: Gradient of $y$ with respect to $\mathbf{x}$
$\int_a^b f(x) ;dx$: Definite integral of $f$ from $a$ to $b$ with respect to $x$
$\int f(x) ;dx$: Indefinite integral of $f$ with respect to $x$

$P(\cdot)$: Probability distribution
$z \sim P$: Random variable $z$ has probability distribution $P$
$P(X \mid Y)$: Conditional probability of $X \mid Y$
$p(x)$: Probability density function
${E}_{x} [f(x)]$: Expectation of $f$ with respect to $x$
$X \perp Y$: Random variables $X$ and $Y$ are independent
$X \perp Y \mid Z$: Random variables $X$ and $Y$ are conditionally independent given random variable $Z$
$\mathrm{Var}(X)$: Variance of random variable $X$
$\sigma_X$: Standard deviation of random variable $X$
$\mathrm{Cov}(X, Y)$: Covariance of random variables $X$ and $Y$
$\rho(X, Y)$: Correlation of random variables $X$ and $Y$
$H(X)$: Entropy of random variable $X$
$D_{\mathrm{KL}}(P|Q)$: KL-divergence of distributions $P$ and $Q$