function from a computational point of view most interesting models that you would just write down intuitively it turns out that it's almost impossible to calculate the amount of probability they assign to a particular point so there's a few different schools of generative models in the likelyhood family one approach is to very carefully design the model so that it is computationally tractable to measure the density it assigns to a particular point so there are things like auto regressive models like pixel CN n those basically break