Empirical distribution in bootstrap method Fushiki (2005) states that

[...] a nonparametric bootstrap sample $x^{* N}=\left\{x_{1}^{*}, \ldots, x_{N}^{*}\right\}$ [is] independently obtained from the empirical
distribution \begin{equation} \hat{p}(x)=\frac{1}{N} \sum_{i=1}^{N}\delta\left(x-x_{i}\right) \end{equation}

I understand how bootstrap works: We sample with replacement from $\left\{x_{1}, \ldots, x_{N}\right\}$. With that in mind, I'm not sure if I understand the mentioned equation. Could you please explain the equation?
 A: The equation says that the distribution is an average of point distributions (Dirac delta functions). For univariate $x$ it's probably easier to think of the CDF and then go from there.
The CDF is
$$\mathbb{F}_n(x) =\Pr(X\leq x) = \frac{1}{n}\sum_{i=1}^N I(X_i\leq x)$$
This is a step function. It takes a step of $1/N$ at each observation, meaning that there's a $1/N$ probability for each observation.
It would be nice if you could have a density function rather than a CDF, but the CDF isn't differentiable, so you can't.  What you can have instead is what's in the equation.  If you naively tried to differentiate the CDF with respect to $x$, you'd get zero at most places and infinite spikes at the observations.  The $\delta$ notation is a way to say something like that, but rigorously.
It says that you get the bootstrap distribution by averaging $N$ other distributions. Each of these other distributions has 100% of its probability at one point: $\delta(x-x_1)$ is notation for "a distribution with 100% probability on $x_1$". So, the notation says that the way  you pick a number from the bootstrap distribution is  to pick one of the $N$ observations, and then choose that number.
The reason this isn't just pointless games with math notation is that it works in more complicated cases.  If $x$ is a point on  the surface of the Earth, you might have to think for quite a while to decide how to define the CDF, but it's easy to say the way  you pick a location from the bootstrap distribution is  to pick one of the $N$ observations, and then choose that location.
