A distribution with an inverse cumulative distribution function (CDF) can be sampled from using just samples from \(U[0, 1]\). The inverse CDF (sometimes called the quantile function) is the value of \(x\) such that \(F_X(x) = Pr(X \leq x) = p\). Consider a that a transformation \(g: [0, 1] \rightarrow \mathbb{R}\), exists which takes a value sampled from the standard uniform distribution \(u \sim U[0, 1]\) and returns a value distributed according to the target distribution. Then the inverse CDF can be written as:

\[Pr(g(U) \leq x) = Pr(U \leq g^{-1}(x)) = g^{-1}(x)\]

Since the CDF of the uniform distribution over the interval \([0, 1]\) is:

\[\begin{align*} F_U(u) = \begin{cases} 0 & u < 0 \\ u & u \in [0, 1) \\ 1 & u \geq 1 \end{cases} \end{align*}\]

Then \(F_x^{-1}(X) = g(x)\) as required. The algorithm below summarises the inverse sampling procedure.

- Sample \(u \sim U[0, 1]\)
- Evaluate \(x = F^{-1}(u)\)
- Return \(x\)

Most statistical packages will expose the quantile function for common distributions making it practical to use inverse sampling. The figure below shows a histogram of 1,000 simulated values from a \(\textrm{Gamma}(3, 4)\) distribution using the inverse CDF method, the analytical density is plotted in red.

```
inverse_cdf_sample <- function(inv_cdf) {
u <- runif(1)
inv_cdf(u)
}
inv_cdf <- function(x) qgamma(p = x, shape = 3, rate = 4)
gamma_samples <- replicate(1000, inverse_cdf_sample(inv_cdf))
qplot(gamma_samples, geom = "blank") +
stat_function(
fun = function(x) dgamma(x, shape = 3, rate = 4),
aes(colour = "Gamma Density")
) +
geom_histogram(aes(y = ..density..), alpha = 0.4) +
theme(
text = element_text(size = 20), legend.title = element_blank(),
legend.text = element_text(size = rel(1.3)), legend.position = c(0.8, 0.8)
) +
ylab("density") +
xlab("value")
```

`## Warning: `mapping` is not used by stat_function()`

`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.`