Cargando…
A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities
We develop a novel computational method for evaluating the extreme excursion probabilities arising from random initialization of nonlinear dynamical systems. The method uses excursion probability theory to formulate a sequence of Bayesian inverse problems that, when solved, yields the biasing distri...
Autores principales: | Rao, Vishwas, Maulik, Romit, Constantinescu, Emil, Anitescu, Mihai |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304705/ http://dx.doi.org/10.1007/978-3-030-50433-5_14 |
Ejemplares similares
-
Quality over quantity: Sampling high probability rare events with the weighted ensemble algorithm
por: Roussey, Nicole M., et al.
Publicado: (2023) -
Machine learning for sampling high-dimensional probability distributions in lattice field theory
por: Shanahan, Phiala
Publicado: (2023) -
Estimating the probabilities of rare arrhythmic events in multiscale computational models of cardiac cells and tissue
por: Walker, Mark A., et al.
Publicado: (2017) -
Python for probability, statistics, and machine learning
por: Unpingco, José
Publicado: (2016) -
Data-Driven Method for Efficient Characterization of Rare Event Probabilities in Biochemical Systems
por: Roh, Min K.
Publicado: (2018)