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03/03/2023
Optimal probabilistic forecasts for risk management
This paper explores the implications of producing forecast distributions...
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02/27/2023
Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models
Stochastic models with global parameters θ and latent variables z are co...
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12/07/2022
Bayesian Forecasting in the 21st Century: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent app...
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10/20/2022
Efficient variational approximations for state space models
Variational Bayes methods are a scalable estimation approach for many co...
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09/29/2022
Bayesian Neural Network Versus Ex-Post Calibration For Prediction Uncertainty
Probabilistic predictions from neural networks which account for predict...
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11/18/2021
Implicit copula variational inference
Key to effective generic, or "black-box", variational inference is the s...
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06/23/2021
A Note on the Accuracy of Variational Bayes in State Space Models: Inference and Prediction
Using theoretical and numerical results, we document the accuracy of com...
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04/29/2021
Loss-Based Variational Bayes Prediction
We propose a new method for Bayesian prediction that caters for models w...
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09/21/2020
Optimal probabilistic forecasts: When do they work?
Proper scoring rules are used to assess the out-of-sample accuracy of pr...
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08/07/2020
Robust Approximate Bayesian Computation: An Adjustment Approach
We propose a novel approach to approximate Bayesian computation (ABC) th...
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05/15/2020
Fast and Accurate Variational Inference for Models with Many Latent Variables
Models with a large number of latent variables are often used to fully u...
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12/29/2019
Focused Bayesian Prediction
We propose a new method for conducting Bayesian prediction that delivers...
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04/16/2019
High-dimensional copula variational approximation through transformation
Variational methods are attractive for computing Bayesian inference for ...
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12/26/2017