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Found 11 papers, 3 papers with code
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport
Dynamic transportation networks have been analyzed for years by means of static graph-based indicators in order to study the temporal evolution of relevant network components, and to reveal complex dependencies that would not be easily detected by a direct inspection of the data.
Spatial 3D Matérn priors for fast whole-brain fMRI analysis
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data.
Methodology Applications Computation
Subsampling MCMC - An introduction for the survey statistician
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work.
Hamiltonian Monte Carlo with Energy Conserving Subsampling
The key insight in our article is that efficient subsampling HMC for the parameters is possible if both the dynamics and the acceptance probability are computed from the same data subsample in each complete HMC iteration.
Tree Ensembles with Rule Structured Horseshoe Regularization
The aggressive noise shrinkage of our prior also makes it possible to complement the rules from boosting in Friedman and Popescu (2008) with an additional set of trees from random forest, which brings a desirable diversity to the ensemble.
The block-Poisson estimator for optimally tuned exact subsampling MCMC
A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using a block-Poisson estimator.
DOLDA - a regularized supervised topic model for high-dimensional multi-class regression
Generating user interpretable multi-class predictions in data rich environments with many classes and explanatory covariates is a daunting task.
Scalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator
We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations.
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods.
Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties.
Speeding Up MCMC by Efficient Data Subsampling
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for $n$ observations is estimated from a random subset of $m$ observations.
