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Found 7 papers, 1 papers with code
Hybrid unadjusted Langevin methods for high-dimensional latent variable models
The method employs the Fisher identity to integrate out the latent variables, which makes it accurate and computationally feasible when applied to big data.
Optimal probabilistic forecasts for risk management
This paper explores the implications of producing forecast distributions that are optimized according to scoring rules that are relevant to financial risk management.
Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models
We show that this is a well-defined natural gradient optimization algorithm for the joint posterior of $(\bm{z},\bm{\theta})$.
Bayesian Forecasting in Economics and Finance: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting.
Bayesian Neural Network Versus Ex-Post Calibration For Prediction Uncertainty
Probabilistic predictions from neural networks which account for predictive uncertainty during classification is crucial in many real-world and high-impact decision making settings.
Scalable Bayesian estimation in the multinomial probit model
Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives.
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series
We propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins.
