
A practical tutorial on Variational Bayes
This tutorial gives a quick introduction to Variational Bayes (VB), also...
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Efficient Selection Between Hierarchical Cognitive Models: Crossvalidation With Variational Bayes
Model comparison is the cornerstone of theoretical progress in psycholog...
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Adaptive Multilevel Hypergradient Descent
Adaptive learning rates can lead to faster convergence and better final ...
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A Bayesian Long ShortTerm Memory Model for Value at Risk and Expected Shortfall Joint Forecasting
ValueatRisk (VaR) and Expected Shortfall (ES) are widely used in the f...
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Assessment and adjustment of approximate inference algorithms using the law of total variance
A common method for assessing validity of Bayesian sampling or approxima...
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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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Identifying relationships between cognitive processes across tasks, contexts, and time
It is commonly assumed that a specific testing occasion (task, design, p...
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Variational Bayes on Manifolds
Variational Bayes (VB) has become a versatile tool for Bayesian inferenc...
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Timeevolving psychological processes over repeated decisions
Many psychological experiments have participants repeat a simple task. T...
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Robustly estimating the marginal likelihood for cognitive models via importance sampling
Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of B...
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A long shortterm memory stochastic volatility model
Stochastic Volatility (SV) models are widely used in the financial secto...
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Manifold Optimisation Assisted Gaussian Variational Approximation
Variational approximation methods are a way to approximate the posterior...
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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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Adversarial Robustness Toolbox v0.2.2
Adversarial examples have become an indisputable threat to the security ...
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New Estimation Approaches for the Linear Ballistic Accumulator Model
The Linear Ballistic Accumulator (LBA) model of Brown (2008) is used as ...
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Bayesian Deep Net GLM and GLMM
Deep feedforward neural networks (DFNNs) are a powerful tool for functio...
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Subsampling Sequential Monte Carlo for Static Bayesian Models
Our article shows how to carry out Bayesian inference by combining data ...
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Flexible Density Tempering Approaches for State Space Models with an Application to Factor Stochastic Volatility Models
Duan (2015) propose a tempering or annealing approach to Bayesian infere...
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Hamiltonian Monte Carlo with Energy Conserving Subsampling
Hamiltonian Monte Carlo (HMC) has recently received considerable attenti...
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Exact Subsampling MCMC
Speeding up Markov Chain Monte Carlo (MCMC) for data sets with many obse...
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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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Variational approximation for heteroscedastic linear models and matching pursuit algorithms
Modern statistical applications involving large data sets have focused a...
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Model Selection by Loss Rank for Classification and Unsupervised Learning
Hutter (2007) recently introduced the loss rank principle (LoRP) as a ge...
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The Loss Rank Criterion for Variable Selection in Linear Regression Analysis
Lasso and other regularization procedures are attractive methods for var...
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