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Pathspace Kalman Filters with Dynamic Process Uncertainty for Analyzing Time-course Data
Here, we develop an extension of the KF, called a Pathspace Kalman Filter (PKF) which allows us to a) dynamically track the uncertainties associated with the underlying data and prior knowledge, and b) take as input an entire trajectory and an underlying mechanistic model, and using a Bayesian methodology quantify the different sources of uncertainty.
Autonomous Learning of Generative Models with Chemical Reaction Network Ensembles
Can a micron sized sack of interacting molecules autonomously learn an internal model of a complex and fluctuating environment?
Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines
These results illustrate how a biochemical computer can use intrinsic chemical noise to perform complex computations.
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