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Found 6 papers, 3 papers with code
Partially factorized variational inference for high-dimensional mixed models
We also provide generic results, which are of independent interest, relating the accuracy of variational inference to the convergence rate of the corresponding coordinate ascent variational inference (CAVI) algorithm for Gaussian targets.
Bayesian prediction of jumps in large panels of time series data
We take a new look at the problem of disentangling the volatility and jumps processes of daily stock returns.
Scalable inference for crossed random effects models
We analyze the complexity of Gibbs samplers for inference in crossed random effect models used in modern analysis of variance.
Dimension-Robust MCMC in Bayesian Inverse Problems
One popular formulation of such problems is as Bayesian inverse problems, where a prior distribution is used to regularize inference on a high-dimensional latent state, typically a function or a field.
Auxiliary gradient-based sampling algorithms
We introduce a new family of MCMC samplers that combine auxiliary variables, Gibbs sampling and Taylor expansions of the target density.
A General Framework for the Parametrization of Hierarchical Models
In this paper, we describe centering and noncentering methodology as complementary techniques for use in parametrization of broad classes of hierarchical models, with a view to the construction of effective MCMC algorithms for exploring posterior distributions from these models.
Methodology
