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The acceptance-rejection algorithm is described as follows:

  • suppose you have RVs $X$ and $Y$ with densities $f_X$ and $f_Y$, respectively, and there exists a constant $c$ such that $\frac{f_X(t)}{f_Y(t)} \leq c$ for all $t,$ then
    1. generate random $y$ from distribution with density $f_Y$
    2. generate random $u$ from $\text{Uniform}(0, 1)$
    3. if $u < \dfrac{f_X(y)}{cf_Y(y)}$ accept $y$ and deliver $x = y$, otherwise repeat

As the generating process describe, we denote the generated RV $Z$ should have the PDF $f_Z(x)$:

$$f_Z(Z = x) = f_Y(Y = y)f_{U|Y}\left(U < \dfrac{f_X(Y)}{cf_Y(Y)}\Big|Y = y\right) = f_{Y,U}\left(Y = y, U < \dfrac{f_X(Y)}{cf_Y(Y)}\right).$$

But the correct deduction seems:

$$f_Z(Z = x) = f_{Y|U}\left(Y = x\Big|U < \dfrac{f_X(Y)}{cf_Y(Y)}\right) = f_X(X=x).$$

I know how to prove the last equation, however cannot understand why it is the generated RV.

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The accepted $X$ can be written as $$X=Y_1\mathbb I_{U_1\le f_X(Y_1)/c f_Y(Y_1)}+Y_2\mathbb I_{U_1> f_X(Y_1)/c f_Y(Y_1)}\mathbb I_{U_2\le f_X(Y_2)/c f_Y(Y_2)}+\cdots$$ It is therefore the transform of the whole sequence $(Y_1,U_1,Y_2,U_2,Y_3,\ldots)$ and not of a single pair $(Y_1,U_1)$. To derive the distribution of such an $X$, one cannot proceed by a change of variable Jacobian formula (as attempted in the first formula) but rather compute the cdf $\mathbb P(X\le x)$ as*: \begin{align}\mathbb P(Y_1\le x,&U_1\le f_X(Y_1)/c f_Y(Y_1))\\ &+\mathbb P(Y_2\le x,U_1>f_X(Y_1)/c f_Y(Y_1),U_2\le f_X(Y_2)/c f_Y(Y_2))+\cdots\\ &=\int_{-\infty}^x \frac{f_X(y)}{c}\,\text{d}y+(1-c^{-1})\int_{-\infty}^x \frac{f_X(y)}{c}\,\text{d}y+\cdots\\ &=\int_{-\infty}^x f_X(y)\,\text{d}y\,c^{-1}\left[1+(1-c^{-1})+(1-c^{-1})^2+\cdots\right]\\ &=\int_{-\infty}^x f_X(y)\,\text{d}y \end{align}


* This is an illustration in dimension one. In larger dimensions consider instead $X\in A$.

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  • $\begingroup$ agree with your deduction, however is there any simply understanding to illustrate the fact $P(X\leq x) = P(Y_1\leq x,U_1\le f_X(Y_1)/c f_Y(Y_1))+ P(Y_2\leq x,U_1>f_X(Y_1)/c f_Y(Y_1),U_2\leq f_X(Y_2)/c f_Y(Y_2))+\cdots = P(Y\leq x|U\leq \dfrac{f_X(Y)}{cf_Y(Y)})?$ $\endgroup$ Mar 12 '20 at 19:09
  • $\begingroup$ The events in each parenthesis are mutually exclusive, hence the probability of the union is the sum of the probabilities. $\endgroup$
    – Xi'an
    Mar 13 '20 at 9:44

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