Variational Parameter

Variational parameters are adjustable values within probabilistic models used to approximate complex probability distributions, primarily aiming to efficiently estimate posterior distributions in Bayesian inference. Current research focuses on optimizing these parameters using advanced techniques like natural gradient descent, normalizing flows, and Wasserstein gradient flows, often within the context of specific model architectures such as neural networks and quantum circuits. These improvements enhance the accuracy and efficiency of variational inference, impacting diverse fields including machine learning, experimental design, and the solution of partial differential equations by enabling more effective handling of high-dimensional data and complex models.