
Variance reduction for distributed stochastic gradient MCMC
Stochastic gradient MCMC methods, such as stochastic gradient Langevin d...
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Scalable Bayesian Nonlinear Matrix Completion
Matrix completion aims to predict missing elements in a partially observ...
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Metaanalysis of Bayesian analyses
Metaanalysis aims to combine results from multiple related statistical ...
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Embarrassingly parallel MCMC using deep invertible transformations
While MCMC methods have become a main workhorse for Bayesian inference,...
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Distributed Bayesian Matrix Factorization with Minimal Communication
Bayesian matrix factorization (BMF) is a powerful tool for producing low...
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Bayesian inference in hierarchical models by combining independent posteriors
Hierarchical models are versatile tools for joint modeling of data sets ...
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Modellingbased experiment retrieval: A case study with gene expression clustering
Motivation: Public and private repositories of experimental data are gro...
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Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
A common approach for Bayesian computation with big data is to partition...
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Paul Blomstedt
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