What is the best way to discretize a 1D continuous random variable? Say I have a 1-dimensional continuous random variable $X$, with PDF $f(X)$, CDF $F(X)$ and inverse CDF $F^{-1}$. What is the best way to discretize $X$? 
To keep things clear, let $Y$ denote the discretized version of $X$, and let $g(Y)$, $G(Y)$ and $G^{-1}$ refer to the  PMF, CDF and inverse CDF of $Y$. 
Ideally, I would like for $E[X] = E[Y]$ and $Var(X) = Var(Y)$. However, I understand if these properties cannot hold for finite $N$ due to discretization error. 
Some thoughts:


*

*Should I just discretize $X$ into $N$ equally spaced points? Or should I discretize more at regions of high probability, by first discretizing a Uniform $[0,1]$ into equidistant points $u_1,u_2...u_N$ and then generating discrete values $Y_1 = F^{-1}(u_1), Y_2 =  F^{-1}(u_2)$ and $Y_N = F^{-1}(u_N)$

*Say I have discretized values $Y_1, Y_2... Y_N$. What is the best way to create $g(Y)$, $G(Y)$ and $G^{-1}$ from these values? My thoughts here are that each $Y_k$ corresponds to an interval of $X$ with width $w_k$, and that the $Y_k$ should be the "midpoints" of these intervals. That is, $g(Y_k) =  p(Y_k - w_k   < X < Y_k + w_k) = F(Y_k + w_k) - F(Y_k - w_k)$. 
 A: Hint: quantization might be a better keyword to search information.
Designing an "optimal" quantization requires some criterion. To try to conserve  the first moment of the discretized variable ... sounds interesting, but I don't think it's very usual.
More frequently (especially if we assume a probabilistic model, as you do) one tries to minimize some distortion: we want the discrete variable to be close to the real one, in some sense. If we stipulate minimum average squared error (not always the best error measure, but the most tractable), the problem is well known, and we can easily build a non-uniform quantizer with minimum rate distortion, if we know the probability of the source; this is almost a synonym of "Max Lloyd quantizer". 
Because a non-uniform quantizer (in 1D) is equivalent to pre-applying a non-linear transformation to a uniform quantizer, this kind of transformation ("companding") (in probabilistic terms, a function that turns our variable into a quasi-uniform) are very related to non uniform quantization (sometimes the concepts are used interchangeably). A pair of venerable examples are the u-Law and A-Law specifications for telephony.
A: Here is one simple idea that may work. If $X$ has distribution $F$, draw a "large" i.i.d. sample $(x_1,\dots,x_n)$ from $F$. Construct the empirical distribution function of this sample as
$$
  \hat{F_n}(t) = \frac{1}{n} \sum_{i=1}^n I_{[x_i,\infty)}(t) \, ,
$$
and treat $\hat{F_n}$ as the distribution function of $Y$, the "discretization" of $X$. This way, $Y$ assumes the values $x_1,\dots,x_n$ with equal probability $1/n$. How large must $n$ be, will depend on the details of your application. I don't claim that this is "best" in any way.
