
Bayesian Optimization of Hyperparameters when the Marginal Likelihood is Estimated by MCMC
Bayesian models often involve a small set of hyperparameters determined ...
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When are Bayesian model probabilities overconfident?
Bayesian model comparison is often based on the posterior distribution o...
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A Bayesian Dynamic Multilayered Block Network Model
As network data become increasingly available, new opportunities arise t...
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Spectral Subsampling MCMC for Stationary Time Series
Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datase...
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Anatomically informed Bayesian spatial priors for fMRI analysis
Existing Bayesian spatial priors for functional magnetic resonance imagi...
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Spatial 3D Matérn priors for fast wholebrain fMRI analysis
Bayesian wholebrain functional magnetic resonance imaging (fMRI) analys...
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RealTime Robotic Search using Hierarchical Spatial Point Processes
Aerial robots hold great potential for aiding Search and Rescue (SAR) ef...
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Modeling Text Complexity using a MultiScale Probit
We present a novel model for text complexity analysis which can be fitte...
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Subsampling MCMC  An introduction for the survey statistician
The rapid development of computing power and efficient Markov Chain Mont...
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Subsampling MCMC  A review for the survey statistician
The rapid development of computing power and efficient Markov Chain Mont...
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Bayesian Sequential Inference in Dynamic Survival Models
Dynamic hazard models are applied to analyze timevarying effects of cov...
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Hamiltonian Monte Carlo with Energy Conserving Subsampling
Hamiltonian Monte Carlo (HMC) has recently received considerable attenti...
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Tree Ensembles with Rule Structured Horseshoe Regularization
We propose a new Bayesian model for flexible nonlinear regression and cl...
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Exact Subsampling MCMC
Speeding up Markov Chain Monte Carlo (MCMC) for data sets with many obse...
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DOLDA  a regularized supervised topic model for highdimensional multiclass regression
Generating user interpretable multiclass predictions in data rich envir...
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Bayesian optimisation for fast approximate inference in statespace models with intractable likelihoods
We consider the problem of approximate Bayesian parameter inference in n...
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Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
Topic models, and more specifically the class of Latent Dirichlet Alloca...
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Speeding Up MCMC by Efficient Data Subsampling
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework...
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Mattias Villani
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