Topic: constant base measure exponential family

Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorithms for extracting latent neural trajectories from neural recordings. However, despite the potential of real-time algorithms to give immediate feedback to experimentalists, and enhance experimental design, they have received markedly less attention compared to their offline counterparts. We introduce the exponential family variational Kalman filter (eVKF), an online recursive Bayesian method aimed at simultaneously i