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---
date: '2019-03-15'
title: List of Notations
template: default
---
Here I list meanings of notations that may have not been explained
elsewhere.
- $\text{ty}$: type. Given a word $w \in [n]^\ell$,
$\text{ty} w = (m_1, m_2, ..., m_n)$ where $m_i$ is the number of
$i$\'s in $w$. For example
$\text{ty} (1, 2, 2, 1, 4, 2) = (2, 3, 0, 1)$. The definition of
$\text{ty} T$ for a tableau $T$ is similar.
- $[n]$: for $n \in \mathbb N_{>0}$, $[n]$ stands for the set
$\{1, 2, ..., n\}$.
- $i : j$: for $i, j \in \mathbb Z$, $i : j$ stands for the set
$\{i, i + 1, ..., j\}$, or the sequence $(i, i + 1, ..., j)$,
depending on the context.
- $k = i : j$: means $k$ iterates over $i$, $i + 1$,\..., $j$. For
example $\sum_{k = 1 : n} a_k := \sum_{k = 1}^n a_k$.
- $x_{i : j}$: stands for the set $\{x_k: k = i : j\}$ or the sequence
$(x_i, x_{i + 1}, ..., x_j)$, depending on the context. So are
notations like $f(i : j)$, $y^{i : j}$ etc.
- $\mathbb N$: the set of natural numbers / nonnegative integer
numbers $\{0, 1, 2,...\}$, whereas
- $\mathbb N_{>0}$ or $\mathbb N^+$: Are the set of positive integer
numbers.
- $x^w$: when both $x$ and $w$ are tuples of objects, this means
$\prod_i x_{w_i}$. For example say $w = (1, 2, 2, 1, 4, 2)$, and
$x = x_{1 : 7}$, then $x^w = x_1^2 x_2^3 x_4$.
- $LHS$, LHS, $RHS$, RHS: left hand side and right hand side of a
formula
- $e_i$: the $i$th standard basis in a vector space:
$e_i = (0, 0, ..., 0, 1, 0, 0, ...)$ where the sequence is finite or
infinite depending on the dimension of the vector space and the $1$
is the $i$th entry and all other entries are $0$.
- $1_{A}(x)$ where $A$ is a set: an indicator function, which
evaluates to $1$ if $x \in A$, and $0$ otherwise.
- $1_{p}$: an indicator function, which evaluates to $1$ if the
predicate $p$ is true and $0$ otherwise. Example: $1_{x \in A}$,
same as $1_A(x)$.
- $\xi \sim p$: the random variable $xi$ is distributed according to
the probability density function / probability mass function /
probability measure $p$.
- $\xi \overset{d}{=} \eta$: the random variables $\xi$ and $\eta$
have the same distribution.
- $\mathbb E f(\xi)$: expectation of $f(\xi)$.
- $\mathbb P(A)$: probability of event $A$.
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