Comparative Analysis of Dual Algorithms for high-dimensional Stopping Problems
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Updated
Jul 2, 2024 - Jupyter Notebook
Comparative Analysis of Dual Algorithms for high-dimensional Stopping Problems
Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.
A python library for sampling image textures from the anisotropic fractional Brownian field.
Companion notes with the numerics for the article on "Improved error estimates for the order of convergence of the Euler method for random ordinary differential equations driven by semi-martingale noises" by Peter E. Kloeden and Ricardo M. S. Rosa
Replication of the research paper : "Discretization of continuous-time arbitrage strategies in financial markets with fBm""
Perlin Simplex Noise implementation
JavaScript implementation of simplex noise 2D algorithm with fBm and SplitMix32 in 550 bytes.
Generate patterns using the fractional Brownian motion
Analysis of point process driven by fractional Brownian motion.
The 2D noise mixer is designed to make mixing different types noise quick with only a few lines of code. Highly commented and easy to use, this system types to be non-platform specific.
Exact methods for simulating fractional Brownian motion and fractional Gaussian noise in python
Fractional brownian motion generator, in C#/dotnet.
Fractional Brownian Motion crate for Rust. An attempt to keep parity with the Python fpm module.
R package for simulating paths of Fractional Brownian Motion and samples of Fractional Gaussian Noise.
Python implementation of fractional brownian motion
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