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Found 8 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.
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance
From the classical and influential works of Neal (1996), it is known that the infinite width scaling limit of a Bayesian neural network with one hidden layer is a Gaussian process, when the network weights have bounded prior variance.
Merging Two Cultures: Deep and Statistical Learning
We show that prediction, interpolation and uncertainty quantification can be achieved using probabilistic methods at the output layer of the model.
Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions
A key benefit of the Mat\'ern class is that it is possible to get precise control over the degree of mean-square differentiability of the random process.
Horseshoe Regularization for Machine Learning in Complex and Deep Models
We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.
Divide and Recombine for Large and Complex Data: Model Likelihood Functions using MCMC
The density parameters are estimated by fitting the density to MCMC draws from each subset DM likelihood function, and then the fitted densities are recombined.
Lasso Meets Horseshoe
The goal of our paper is to survey and contrast the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization methodologies.
Methodology Primary 62J07, 62J05, Secondary 62H15, 62F03
Horseshoe Regularization for Feature Subset Selection
Feature subset selection arises in many high-dimensional applications of statistics, such as compressed sensing and genomics.
