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Found 9 papers, 2 papers with code
Probabilistic Survival Analysis by Approximate Bayesian Inference of Neural Networks
In this paper, we study the benefits of modeling uncertainty in deep neural networks for survival analysis with a focus on prediction and calibration performance.
Variational Inference for GARCH-family Models
The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling.
Bayesian Learning for Neural Networks: an algorithmic survey
The last decade witnessed a growing interest in Bayesian learning.
Manifold Gaussian Variational Bayes on the Precision Matrix
We propose an optimization algorithm for Variational Inference (VI) in complex models.
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots
Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series.
Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning
Our Quasi Black-box Variational Inference (QBVI) framework is readily applicable to a wide class of Bayesian inference problems and is of simple implementation as the updates of the variational posterior do not involve gradients with respect to the model parameters, nor the prescription of the Fisher information matrix.
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets
The prediction of financial markets is a challenging yet important task.
Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction
Financial time-series forecasting is one of the most challenging domains in the field of time-series analysis.
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders.
