Stochastic-process Libraries

3 Components & Libraries

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Stochastic-process Libraries

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations…

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

To engage in conversations about uncertainty quantification, or ask question about UQpy usage and functionality refer to the UQpy's discussions tab: UQpy (Uncertainty Quantification with python) is a…

Implement pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston

Implementation of pricing analytics and Monte Carlo simulations for modeling of options and implied volatilities. The StochVolPackage provides: For the analytic implementation of stochastic volatilit…

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