Sample quality for intractable normalizing function algorithms

Music City Center 

Models with intractable normalizing functions arise in a wide variety of areas, for instance network models, spatial models for lattices and point processes, flexible models for count data and gene expression, and models for permutations. Some of the most practical algorithms for these problems do not have rigorous theoretical justifications so it is difficult to determine how to tune them or assess their quality. I will discuss new diagnostics that are useful for assessing the quality of samples from these algorithms. These diagnostics are also useful for tuning the algorithms and they provide general insights about some popular intractable likelihood algorithms.