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.

Ministry of Random Walks

Construction of Wiener process sample paths in Matlab using the wavelet method.

Implementation of Image Restoration Techniques for "EE 702: Image Processing" course assignment

Exercise solution to Stochastic Processes course

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.

Investigating Wiener Processes

Uses different algorithms to generate sounds derived from SoundFont presets

Reinforcement learning models of human decision making in time allocation behavioral experiments

Simulation of the quantum trajectories of Schrödinger cat states

Random walk in d dimentions and central limit theory.

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.

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.

Stochastic Processes: Basic Examples