Latex in markdown reference

Reference

https://en.wikibooks.org/wiki/LaTeX/Mathematics

Examples

We can integrate markdown between $ $ or $$ $$

Single $ will be inline

Double $$ will be centered into a new line

LaTeX	Markdow
$\hat{q}$	\widehat{q}
$|x-x_M|$	|x-x_M|
$f_M(x)$	f_M(x)
${\displaystyle \sum _m}$	\displaystyle\sum_{m}
$⟨x,w_m⟩$	\langle{x,w_m}\rangle
$\parallel v_m\parallel$	\| v_m \|
$\cos$	\cos
${\mathbb{Z}}^d$	\mathbb{Z}$^d
$\mathrm{\infty }$	\infty
$f\in L^2$	f \in L^2
$$	\implies
$\underset{M\to \mathrm{\infty }}{lim}$	\lim\limits_{M \to \infty}
$P(A|B)=\frac{P(B|A)\ast P(A)}{P(B)}$	P(A|B) = \frac{P(B|A)*P(A)}{P(B)}
$\begin{array}{r}\\ Q_t(a)& =\frac{\text{sum of rewards when }\mathit{a}\text{ taken prior to }\mathit{t}}{\text{number of times }\mathit{a}\text{ taken prior to }\mathit{t}}\\ & =\frac{{\displaystyle \sum _{i=1}^{t-1}{R}_i.{\mathcal{1}}_{A_i=a}}}{{\displaystyle \sum _{i=1}^{t-1}{\mathcal{1}}_{A_i=a}}}\end{array}$	\begin{align} \\  Q_t(a) &= \frac{\text{sum of rewards when } \mathit{a} \text{ taken prior to }\mathit{t}}{\text{number of times } \mathit{a} \text{ taken prior to }\mathit{t}} \\  & = \frac{\displaystyle\sum_{i=1}^{t-1} R_i.\mathcal{1}_{A_i=a}}{\displaystyle\sum_{i=1}^{t-1} \mathcal{1}_{A_i=a}}  \end{align}
$A_t=\underset{a}{\mathrm{argmax}}{Q}_t(a)$	A_t=\underset{a}{\mathrm{argmax}}{\text{ }Q_t(a)}
$p(s^{\prime },r|s,a)\doteq Pr\{S_t=s^{\prime },R_t=r|S_{t-1}=s,A_{t-1}=a\}$	p(s',r|s,a) \doteq Pr\{S_t=s', R_t=r|S_{t-1}=s, A_{t-1}=a\}
$q_{\pi }(s,a)\doteq \mathbb{E}[R_{t+1}+\gamma .G_{t+1}|S_t=s,A_t=a]$	q_\pi(s,a) \doteq \mathbb{E}[R_{t+1}+\gamma.G_{t+1}|S_t=s, A_t=a]
$v_{\ast }(s)\doteq \underset{\pi }{max}{v}_{\pi }(s),\mathrm{\forall }s\in S$	v_*(s)\doteq \max\limits_{\pi} v_\pi(s), \forall s \in S
$q_{\pi }(s,a)\doteq \mathbb{E}[R_{t+1}+\gamma .G_{t+1}|S_t=s,A_t=a]$	q_\pi(s,a) \doteq \mathbb{E}[R_{t+1}+\gamma.G_{t+1}|S_t=s, A_t=a]
$l(w,b)=\frac{1}{N}{\displaystyle \sum _{n=1}^N(y_n-(x_{n}w+b){)}^2}$	l(w,b)=\frac{1}{N}\displaystyle\sum_{n=1}^{N}(y_n-(x_nw+b))^2
$\mathrm{\nabla }l(w,b)=\left[\begin{array}{c}\frac{\partial l(w,b)}{\partial w_1}\\ ⋮\\ \frac{\partial l(w,b)}{\partial w_d}\end{array}\right]$	\nabla l(w,b) = \begin{bmatrix}\frac{\partial l(w,b)}{\partial w_1}\\ \vdots \\\frac{\partial l(w,b)}{\partial w_d}\end{bmatrix}
$H(X)=–\sum _{x\in X}P(x)\ast \log (P(x))$	\\ H(X) = – \sum_{x \in X} P(x) * \log(P(x))
$X\sim \mathcal{N}(\mu ,{\sigma }^2)$	X \sim \mathcal{N}(\mu,\,\sigma^{2})
$\sqrt[n]{1+x+x^2+x^3+\cdots +x^n}$	\sqrt[n]{1+x+x^2+x^3+\dots+x^n}