2017
DOI: 10.1103/physreve.95.043303
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Classification framework for partially observed dynamical systems

Abstract: We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space. In contrast to the existing approaches using model point estimates to represent individual data items, we employ posterior distributions over models, thus taking into account in a principled manner the uncertainty due to both the generative (observational and/or dynamic noise) and observation (sampling in time) processes. We evaluate the framework on two testbeds -a biological p… Show more

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Cited by 11 publications
(6 citation statements)
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“…We note that the linear response formulation assumes Markovian bath dynamics. Recent work has shown additional corrections are required for non-Markovian baths (as also when the initial state has system-bath correlations) [51,52]. We shall neglect such small effects in this work.…”
Section: Linear Response and Absorption Spectrummentioning
confidence: 99%
“…We note that the linear response formulation assumes Markovian bath dynamics. Recent work has shown additional corrections are required for non-Markovian baths (as also when the initial state has system-bath correlations) [51,52]. We shall neglect such small effects in this work.…”
Section: Linear Response and Absorption Spectrummentioning
confidence: 99%
“…Although some advances have been made in modelling these interactions [18,26], uncovering the network architecture between these pathways after major surgery and in critical illness has so far not been achieved. To do so will require a larger cohort of patients and a combination of mathematical modelling and machine learning techniques [32].…”
Section: Discussionmentioning
confidence: 99%
“…Although some advances have been made in modelling these interactions 9,28 , uncovering the network architecture between these pathways after major surgery and in critical illness has so far not been achieved. To do so will require a larger cohort of patients and a combination of mathematical modelling and machine learning techniques 29 .…”
Section: Discussionmentioning
confidence: 99%