31 citations · 80 across the 14 of their papers we have counts for
15 papers
Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models
Weiben Zhang, Michael Stanley Smith, Worapree Maneesoonthorn +1
Stochastic models with global parameters and latent variables are common, and for which variational inference (VI) is popular. However, existing methods are often either slow or in…
Implicit copula variational inference
Michael Stanley Smith, Rubén Loaiza-Maya
Key to effective generic, or "black-box", variational inference is the selection of an approximation to the target density that balances accuracy and speed. Copula models are promi…
Implicit Copulas: An Overview
Michael Stanley Smith
Implicit copulas are the most common copula choice for modeling dependence in high dimensions. This broad class of copulas is introduced and surveyed, including elliptical copulas,…
Variational inference for cutting feedback in misspecified models
Xuejun Yu, David J. Nott, Michael Stanley Smith
Bayesian analyses combine information represented by different terms in a joint Bayesian model. When one or more of the terms is misspecified, it can be helpful to restrict the use…
Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices
Nadja Klein, Michael Stanley Smith, David J. Nott
Recurrent neural networks (RNNs) with rich feature vectors of past values can provide accurate point forecasts for series that exhibit complex serial dependence. We propose two app…
Fast and Accurate Variational Inference for Models with Many Latent Variables
Rubén Loaiza-Maya, Michael Stanley Smith, David J. Nott +1
Models with a large number of latent variables are often used to fully utilize the information in big or complex data. However, they can be difficult to estimate using standard app…