Probabilistic Models, Sampling Methods, Machine Learning, Editors Pick, Hands On Tutorials.
approximate our complex function by taking some well-defined distribution Q(x), which by multiplying with some positive integer c, will always lie above our distribution of interest P(x) on the Cartesian axis, and take samples from a uniformly-distributed range of axis values from a vertical slice