The informed sampler: A discriminative approach to Bayesian inference in generative computer vision models
The informed sampler  a general inference technique for Bayesian posterior inference in generative models. This method leverages discriminative computer vision models for faster probabilistic inference in generative models. Three different applications that highlight common challenges of posterior inference. Detailed comparisons and analysis with respect to different baseline sampling based methods. Informed sampling is found to converge faster than all baseline samplers across diverse problems.
generative models probabilistic models mcmc inference inverse graphics