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Found 3 papers, 3 papers with code
Likelihood Based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants
Probabilistic graphical models that encode an underlying Markov random field are fundamental building blocks of generative modeling to learn latent representations in modern multivariate data sets with complex dependency structures.
Nearest Neighbor Dirichlet Mixtures
There is a rich literature on Bayesian methods for density estimation, which characterize the unknown density as a mixture of kernels.
Fast sampling with Gaussian scale-mixture priors in high-dimensional regression
We propose an efficient way to sample from a class of structured multivariate Gaussian distributions which routinely arise as conditional posteriors of model parameters that are assigned a conditionally Gaussian prior.
Computation
