7 citations · 15 across the 15 of their papers we have counts for
4 papers · 1 filter
Bayesian Forecasting in Economics and Finance: A Modern Review
Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn +6
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem…
Efficient variational approximations for state space models
Rubén Loaiza-Maya, Didier Nibbering
Variational Bayes methods are a potential scalable estimation approach for state space models. However, existing methods are inaccurate or computationally infeasible for many state…
Bayesian Neural Network Versus Ex-Post Calibration For Prediction Uncertainty
Satya Borgohain, Klaus Ackermann, Ruben Loaiza-Maya
Probabilistic predictions from neural networks which account for predictive uncertainty during classification is crucial in many real-world and high-impact decision making settings…
Fast variational Bayes methods for multinomial probit models
Rubén Loaiza-Maya, Didier Nibbering
The multinomial probit model is often used to analyze choice behaviour. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which l…