  The number of sampling methods could be daunting for a practitioner looking
to cast powerful machine learning methods to their specific problem. This paper
takes a theoretical stance to review and organize many sampling approaches in
the ``generative modeling'' setting, where one wants to generate new data that
are similar to some training examples. By revealing links between existing
methods, it might prove useful to overcome some of the current challenges in
sampling with diffusion models, such as long inference time due to diffusion
simulation, or the lack of diversity in generated samples.
