High-frequency statistical arbitrage
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
Predicting stock prices using Geometric Brownian Motion and the Monte Carlo method
Julia stochastic processes package.
Construction of Wiener process sample paths in Matlab using the wavelet method.
Implementation of Image Restoration Techniques for "EE 702: Image Processing" course assignment
Ministry of Random Walks
Exercise solution to Stochastic Processes course
Investigating Wiener Processes
Uses different algorithms to generate sounds derived from SoundFont presets
An example inspire by a recent post on Brownian motion GIF generation with R. Some tweaks and fixes to the original code an explanation in the README.
MATLAB implementations of Kalman filtering, spectral estimation, stochastic processes, and statistical detection — from first principles.
Predicting headache occurrences using Hidden Markov Models (HMMs). The dataset comprises 296 days of binary headache records. Analysis reveals cyclical patterns, with the DTMC Model yielding the best predictions. Wiener Process, sARIMA, Bayesian Normal Mixtures, DTMC, and Categorical Mixtures models are also reported.
From Random Walks to Brownian Motion.
wiener process.
Python simulations of stochastic processes using Wiener processes, including rotating-capillary motion and snow accumulation models.
R package for statistical inference, Wiener degradation modeling with drift, and reliability analysis of repairable systems under imperfect maintenance (ARD1). Exact MLE, RUL prognosis via Inverse Gaussian FHT, and publication-ready diagnostics.
Gives analytic formulas to calculate autocovariance matrix and autocorrelation matrix for averaged Wiener process with equal-distance time points. Is supplemented with Python numpy code to verify those formulas with a Monte Carlo simulation.
Random walk in d dimentions and central limit theory.
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