Linear regression takes the logic of the correlation coefficient and extends it to a predictive model of that relationship. Some key advantages of linear regression are that it can be used to predict ...

Package for the data-driven representation of non-linear dynamics over manifolds based on a statistical distribution of local phase portrait features. Includes specific example on dynamical systems, ...

Topics include methods of solutions for linear and non-linear first order differential equations, linear second order differential equations, higher order linear differential equations, systems of ...

but this is not easy even in the linear case if the random term is not a Gaussian white noise. A universal method is developed here to handle both Gaussian and compound Poisson white noise. The ...

CUET PG Mathematics Syllabus 2025 is designed to evaluate the aspirant’s proficiency in core mathematical concepts and ...

A differentiable variant of the Gillespie algorithm enables gradient-based optimization for stochastic chemical kinetics, facilitating efficient parameter estimation and the design of biochemical ...

This is a 2D orthogonal elliptic mesh (grid) generator which works by solving the Winslow partial differential equations ... From this, a suitable linear approximation is obtained, and the adjustment ...

Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=8016795 ...

Recurrent spiking networks that process input stimuli with optimal efficiency have key emerging properties that are similar to those of biological neural networks.