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Found 9 papers, 3 papers with code
Federated Stochastic Gradient Langevin Dynamics
We apply conducive gradients to distributed stochastic gradient Langevin dynamics (DSGLD) and call the resulting method federated stochastic gradient Langevin dynamics (FSGLD).
A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication
We show that the combination of the two methods gives substantial improvements in the scalability of BMF on web-scale datasets, when the goal is to reduce the wall-clock time.
Scalable Bayesian Non-linear Matrix Completion
Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed.
Embarrassingly parallel MCMC using deep invertible transformations
While MCMC methods have become a main work-horse for Bayesian inference, scaling them to large distributed datasets is still a challenge.
Distributed Bayesian Matrix Factorization with Limited Communication
Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals.
Bayesian inference in hierarchical models by combining independent posteriors
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources.
A Bayesian length-based population dynamics model for northern shrimp (Pandalus Borealis)
We introduce a fully length-based Bayesian model for the population dynamics of northern shrimp (Pandalus Borealis).
Applications
Modelling-based experiment retrieval: A case study with gene expression clustering
For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples.
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation.
