Gaussian Prior

Gaussian priors are probability distributions commonly used in Bayesian inference to represent prior knowledge about model parameters, particularly in complex models like neural networks. Current research focuses on extending the applicability of Gaussian priors beyond their limitations in handling non-Gaussian data, high dimensionality, and deep architectures, exploring alternative approaches like implicit priors and function-space priors within Bayesian neural networks and Gaussian processes. This work aims to improve model accuracy, uncertainty quantification, and robustness to outliers, impacting fields like scientific machine learning and anomaly detection by providing more reliable and interpretable models.