no code implementations • 10 Aug 2023 • Lin Deng, Michael Stanley Smith, Worapree Maneesoonthorn
We show that the copula implicit in the skew-t distribution of Azzalini and Capitanio (2003) allows for a higher level of pairwise asymmetric dependence than two popular alternative skew-t copulas.
no code implementations • 27 Feb 2023 • Weiben Zhang, Michael Stanley Smith, Worapree Maneesoonthorn, Ruben Loaiza-Maya
We show that this is a well-defined natural gradient optimization algorithm for the joint posterior of $(\bm{z},\bm{\theta})$.
no code implementations • 5 Oct 2020 • Nadja Klein, Michael Stanley Smith, David J. Nott
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.
no code implementations • 26 Aug 2019 • Nadja Klein, David J. Nott, Michael Stanley Smith
The end result is a scalable distributional DNN regression method with marginally calibrated predictions, and our work complements existing methods for probability calibration.
no code implementations • 26 Dec 2017 • Ruben Loaiza-Maya, Michael Stanley Smith
We propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins.