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Found 10 papers, 0 papers with code
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families
A theoretical analysis shows that dropout regularization prefers rare but important features in both the mean and dispersion, generalizing an earlier result for conventional generalized linear models.
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods.
On a Variational Approximation based Empirical Likelihood ABC Method
The expected log-likelihood is then estimated by an empirical likelihood where the only inputs required are a choice of summary statistic, it's observed value, and the ability to simulate the chosen summary statistics for any parameter value under the model.
Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices
Using data from the Australian National Electricity Market, we show that our deep time series models provide accurate short term probabilistic price forecasts, with the copula model dominating.
Marginally-calibrated deep distributional regression
The end result is a scalable distributional DNN regression method with marginally calibrated predictions, and our work complements existing methods for probability calibration.
An easy-to-use empirical likelihood ABC method
Many scientifically well-motivated statistical models in natural, engineering and environmental sciences are specified through a generative process, but in some cases it may not be possible to write down a likelihood for these models analytically.
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.
Gaussian variational approximation with sparse precision matrices
We consider the problem of learning a Gaussian variational approximation to the posterior distribution for a high-dimensional parameter, where we impose sparsity in the precision matrix to reflect appropriate conditional independence structure in the model.
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
Variational inference for sparse spectrum Gaussian process regression
We develop a fast variational approximation scheme for Gaussian process (GP) regression, where the spectrum of the covariance function is subjected to a sparse approximation.
Computation
