Jun 2, 2022 · The developed method applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe ...
Bayesian inference and optimisation of stochastic dynamical networks
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This paper applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe networks of measured nodes. A ...
Bayesian Inference of Stochastic Dynamical Networks · Yasen Wang, Junyang Jin, J. Gonçalves · Published in arXiv.org 2 June 2022 · Computer Science, Mathematics, ...
Jun 10, 2022 · Refined trajectories are updated iteratively along with network topologies and system dynamics, which greatly improves inference accuracy.
A new methodology for Bayesian inference of stochastic dynamical models is developed. The methodology leverages the dynamically orthogonal (DO) evolution ...
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This paper applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe networks of measured nodes.
Nov 20, 2020 · Stochastic modelling is an important method to investigate the functions of noise in a wide range of biological systems.
The developed method applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe networks of measured ...
A new methodology for rigorous Bayesian learning of high-dimensional stochastic dynamical models is developed.
Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells. These models usually involve ...