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# ##  Inference Methods Our inference method is one of our primary contributions to the state of the art.  ## ###  State of the Art The result of the models above is a probability density. The catch is that unlike with simpler models, the integral representing our density is intractable. One solution makes use of the fact that while intergrating over the whole measure space is difficult, plugging in values and retrieving an (un-normalised) probability is not. We can exploit this property by performing an intelligent random walk over the surface of the density. The idea is that if we walk for long enough, we'll obtain a reasonable representation of the surface. This is the basis for a family of inference methods called Markov-Chain Monte-Carlo (MCMC) sampling.