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09/05/2023
DeepVol: A Deep Transfer Learning Approach for Universal Asset Volatility Modeling
This paper introduces DeepVol, a promising new deep learning volatility ...
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05/24/2023
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood
The Bayesian Synthetic Likelihood (BSL) method is a widely-used tool for...
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03/24/2023
Particle Mean Field Variational Bayes
The Mean Field Variational Bayes (MFVB) method is one of the most comput...
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02/21/2023
Bayesian Inference for Evidence Accumulation Models with Regressors
Evidence accumulation models (EAMs) are an important class of cognitive ...
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02/16/2023
Realized recurrent conditional heteroskedasticity model for volatility modelling
We propose a new approach to volatility modelling by combining deep lear...
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12/13/2021
An Introduction to Quantum Computing for Statisticians and Data Scientists
Quantum computers promise to surpass the most powerful classical superco...
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06/10/2021
Quantum Natural Gradient for Variational Bayes
Variational Bayes (VB) is a critical method in machine learning and stat...
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03/01/2021
A practical tutorial on Variational Bayes
This tutorial gives a quick introduction to Variational Bayes (VB), also...
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02/12/2021
Efficient Selection Between Hierarchical Cognitive Models: Cross-validation With Variational Bayes
Model comparison is the cornerstone of theoretical progress in psycholog...
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08/17/2020
Adaptive Multi-level Hyper-gradient Descent
Adaptive learning rates can lead to faster convergence and better final ...
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01/23/2020
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting
Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the f...
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11/20/2019
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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10/30/2019
Spectral Subsampling MCMC for Stationary Time Series
Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datase...
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10/16/2019
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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08/08/2019
Variational Bayes on Manifolds
Variational Bayes (VB) has become a versatile tool for Bayesian inferenc...
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06/26/2019
Time-evolving psychological processes over repeated decisions
Many psychological experiments have participants repeat a simple task. T...
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06/14/2019
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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06/07/2019
A long short-term memory stochastic volatility model
Stochastic Volatility (SV) models are widely used in the financial secto...
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02/11/2019
Manifold Optimisation Assisted Gaussian Variational Approximation
Variational approximation methods are a way to approximate the posterior...
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07/23/2018
Subsampling MCMC - An introduction for the survey statistician
The rapid development of computing power and efficient Markov Chain Mont...
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07/23/2018
Subsampling MCMC - A review for the survey statistician
The rapid development of computing power and efficient Markov Chain Mont...
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07/03/2018
Adversarial Robustness Toolbox v0.2.2
Adversarial examples have become an indisputable threat to the security ...
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06/26/2018
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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05/25/2018
Bayesian Deep Net GLM and GLMM
Deep feedforward neural networks (DFNNs) are a powerful tool for functio...
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05/08/2018
Subsampling Sequential Monte Carlo for Static Bayesian Models
Our article shows how to carry out Bayesian inference by combining data ...
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05/02/2018
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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08/02/2017
Hamiltonian Monte Carlo with Energy Conserving Subsampling
Hamiltonian Monte Carlo (HMC) has recently received considerable attenti...
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03/27/2016
Exact Subsampling MCMC
Speeding up Markov Chain Monte Carlo (MCMC) for data sets with many obse...
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04/16/2014
Speeding Up MCMC by Efficient Data Subsampling
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework...
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11/22/2010
Variational approximation for heteroscedastic linear models and matching pursuit algorithms
Modern statistical applications involving large data sets have focused a...
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11/05/2010
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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11/05/2010