A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
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Updated
May 25, 2024 - Julia
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 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.
High-frequency statistical arbitrage
Investigating Wiener Processes
Stochastic Processes: Basic Examples
Julia stochastic processes package.
Random walk in d dimentions and central limit theory.
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.
Predicting stock prices using Geometric Brownian Motion and the Monte Carlo method
From Random Walks to Brownian Motion.
Ministry of Random Walks
wiener process.
Implementation of Image Restoration Techniques for "EE 702: Image Processing" course assignment
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.
Exercise solution to Stochastic Processes course
Reinforcement learning models of human decision making in time allocation behavioral experiments
Stochastic particle method for the nonlinear diffusion equation.
We derive asymptotic behavior of the probability of high-level excursion for the maximal increment of the Wiener process.
Scilab
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