24 citations · 34 across the 5 of their papers we have counts for
5 papers
Model selection of chaotic systems from data with hidden variables using sparse data assimilation
H. Ribera, S. Shirman, A. V. Nguyen +1
Many natural systems exhibit chaotic behaviour such as the weather, hydrology, neuroscience and population dynamics. Although many chaotic systems can be described by relatively si…
Strategic Monte Carlo Methods for State and Parameter Estimation in High Dimensional Nonlinear Problems
Sasha Shirman, Henry D. I. Abarbanel
In statistical data assimilation one seeks the largest maximum of the conditional probability distribution of model states, , and…
Machine Learning as Statistical Data Assimilation
H. D. I. Abarbanel, P. J. Rozdeba, S. Shirman
We identify a strong equivalence between neural network based machine learning (ML) methods and the formulation of statistical data assimilation (DA), known to be a problem in stat…
Machine Learning, Deepest Learning: Statistical Data Assimilation Problems
Henry Abarbanel, Paul Rozdeba, Sasha Shirman
We formulate a strong equivalence between machine learning, artificial intelligence methods and the formulation of statistical data assimilation as used widely in physical and biol…
HVC Interneuron Properties from Statistical Data Assimilation
Daniel Breen, Sasha Shirman, Eve Armstrong +2
Data assimilation (DA) solves the inverse problem of inferring initial conditions given data and a model. Here we use biophysically motivated Hodgkin-Huxley (HH) models of avian HV…