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A method is provided for designing and training noise-driven recurrent neural networks as models of stochastic processes.
The discussion is illustrated by the analysis of a number of examples, connected with fundamental models of stochastic fracture mechanics and with random vibration.
We also draw parallels to models of stochastic dynamical systems, and use this to develop a model that deals with iterated update and noisy observations in qualitative settings that is analogous to Bayesian updating in a quantitative setting.
We discuss the status of LIGO, the most recent results of the search for stochastic GW radiation with LIGO interferometers, and the implications of these results for some of the theoretical models of stochastic GW background.
Granger formalized this idea in the context of linear regression models of stochastic processes [4].
However, models of stochastic gene expression commonly consider promoter activity as a two-state on/off system.
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Limited to 60. Description: Analysis and modeling of stochastic processes.
The model was calibrated using a mathematical model of stochastic plant development and growth.
This paper develops and demonstrates a model of stochastic spatial variation.
This article has established a probabilistic distribution model of stochastic fatigue damage.
The excitation is modeled as a bounded noise, which is a realistic model of stochastic fluctuation in engineering applications.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com