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Found 3 papers, 1 papers with code
Efficient Sampling-Based Bayesian Active Learning for synaptic characterization
Bayesian Active Learning (BAL) is an efficient framework for learning the parameters of a model, in which input stimuli are selected to maximize the mutual information between the observations and the unknown parameters.
The Hitchhiker's Guide to Nonlinear Filtering
From there we continue our journey through discrete-time models, which is usually encountered in machine learning, and generalize to and further emphasize continuous-time filtering theory.
Methodology
Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes
We revisit the problem of estimating the parameters of a partially observed diffusion process, consisting of a hidden state process and an observed process, with a continuous time parameter.
