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Found 18 papers, 7 papers with code
ProDAG: Projection-Induced Variational Inference for Directed Acyclic Graphs
Directed acyclic graph (DAG) learning is a rapidly expanding field of research.
Contextual Directed Acyclic Graphs
Estimating the structure of directed acyclic graphs (DAGs) from observational data remains a significant challenge in machine learning.
Data Scaling Effect of Deep Learning in Financial Time Series Forecasting
For years, researchers investigated the applications of deep learning in forecasting financial time series.
Particle Mean Field Variational Bayes
The Mean Field Variational Bayes (MFVB) method is one of the most computationally efficient techniques for Bayesian inference.
Deep Learning Enhanced Realized GARCH
We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures.
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
With this capability gap in mind, we study a not-uncommon situation where the input features dichotomize into two groups: explanatory features, which are candidates for inclusion as variables in an interpretable model, and contextual features, which select from the candidate variables and determine their effects.
Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement
This article investigates retirement decumulation behaviours using the Grouped Fixed-Effects (GFE) estimator applied to Australian panel data on drawdowns from phased withdrawal retirement income products.
Variance reduction properties of the reparameterization trick
From this, we show that the marginal variances of the reparameterization gradient estimator are smaller than those of the score function gradient estimator.
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.
Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives
This article considers the problem of estimating a multivariate probit model in a panel data setting with emphasis on sampling a high-dimensional correlation matrix and improving the overall efficiency of the data augmentation approach.
Computation Applications Methodology
Bayesian Deep Net GLM and GLMM
Efficient computational methods for high-dimensional Bayesian inference are developed using Gaussian variational approximation, with a parsimonious but flexible factor parametrization of the covariance matrix.
Computation
Subsampling Sequential Monte Carlo for Static Bayesian Models
SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the posterior distribution.
Gaussian variational approximation for high-dimensional state space models
The variational parameters to be optimized are the mean vector and the covariance matrix of the approximation.
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.
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.
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.
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.
Efficient variational inference for generalized linear mixed models with large datasets
We propose a divide and recombine strategy for the analysis of large datasets, which partitions a large dataset into smaller pieces and then combines the variational distributions that have been learnt in parallel on each separate piece using the hybrid Variational Bayes algorithm.
Methodology
