{"articles":{"inverse-cdf-sampling@computational-statistical-methods":{"content":"<p>Inverse CDF sampling is a Monte Carlo sampling method. In inverse CDF sampling, random values are sampled from a uniform distribution on \\({{\\left[0..1\\right]}}\\). \\({F}^{-1}\\) is then applied to the sample values to obtain a sample over \\(f\\). <br> This means that a sample from any distribution can be obtained by sampling from a uniform distribution under the hood and knowing \\({F}^{-1}\\). Advantages of this is that implementing a uniform distribution sampler is simpler than most distributions.</p>","names":[[["Inverse CDF sampling",""]]]}},"style":"Method"}