Boltzmann Machines, marginal and conditional probabilities
A generative model can learn to represent and sample from a
probability distribution. The core idea is to learn a parametric model
of the probability distribution from which the training data was
drawn. As an example
- A model for images could learn to draw new examples of cats and dogs, given a training dataset of images of cats and dogs.
- Generate a sample of an ordered or disordered Ising model phase, having been given samples of such phases.
- Model the trial function for Monte Carlo calculations