Automatic probabilistic programming for scientific machine learning and dynamical models
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Updated
Jun 26, 2024 - Julia
Automatic probabilistic programming for scientific machine learning and dynamical models
We present a user-friendly open-source Matlab package for stochastic data analysis that enables to perform a standard analysis of given turbulent data and extracts the stochastic equations describing the scale-dependent cascade process in turbulent flows through Fokker-Planck equations and concepts of non-equilibrium stochastic thermodynamics.
Bayesian inference for Discrete state-space Partially Observed Markov Processes in Julia. See the docs:
Apuntes de Ingeniería Civil Matemática, con electivos en Computación y Aprendizaje.
An Open Source Tool for Analyzing Discrete Markov Chains.
Code used in "Minimizing Information Leakage of Abrupt Changes in Stochastic Systems" , by Alessio Russo, [email protected] .
Generating Multi-State Survival Data
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