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. 2017 Jun 14;14(131):20170332. doi: 10.1098/rsif.2017.0332

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© 2017 The Author(s).

Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

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Figure 2. — Representation of the proposed Bayesian dynamic elastic-net approach as a probabilistic graphical model. The hidden influences w_l form a Markov chain over all time points l = 1, …, T and are directly dependent on the shared parameters and λ₂. Since the outcome of one integration step represents the initial value for the next integration step, the system state variables are also successively dependent.

Inline graphic — Representation of the proposed Bayesian dynamic elastic-net approach as a probabilistic graphical model. The hidden influences w_l form a Markov chain over all time points l = 1, …, T and are directly dependent on the shared parameters and λ₂. Since the outcome of one integration step represents the initial value for the next integration step, the system state variables are also successively dependent.