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Collection of notebooks about quantitative finance, with interactive python code.
Updated
Feb 1, 2021
Jupyter Notebook
NMA Computational Neuroscience course
Updated
Jul 20, 2021
Jupyter Notebook
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components
Updated
Jul 5, 2021
Julia
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
Updated
Jul 20, 2021
Python
Generate realizations of stochastic processes in python.
Updated
Jun 2, 2021
Python
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
Updated
Jul 5, 2021
Julia
Adaptive computational fluid dynamics
R package for statistical inference using partially observed Markov processes
Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
Updated
Feb 10, 2021
Jupyter Notebook
Matlab Toolbox for the Numerical Solution of Stochastic Differential Equations
Updated
Sep 30, 2020
MATLAB
Economic scenario generator for python: simulate stocks, interest rates, and other stochastic processes.
Updated
Mar 19, 2021
Python
Multifractal Detrended Fluctuation Analysis in Python
Updated
Jan 22, 2021
Python
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
Updated
Jul 19, 2021
Julia
Demonstrating the benefits of using Bayesian Inference and PYMC3 for estimating the parameters of stochastic processes commonly used in quantitative finance.
Updated
Feb 18, 2019
Jupyter Notebook
By means of stochastic volatility models
Updated
Mar 24, 2020
Jupyter Notebook
Tools to generate and study moment equations for any chemical reaction network using various moment closure approximations
Updated
Jul 19, 2021
Julia
SdePy: Numerical Integration of Ito Stochastic Differential Equations
Updated
Jan 17, 2021
Python
Jdmbs: An R Package for Monte Carlo Option Pricing Algorithm for Jump Diffusion Models with Correlational Companies
Exercises and notes for N.G. Van Kampen's Stochastic Processes in Physics and Chemistry
PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)
Updated
Nov 5, 2020
Python
Automating various decisions stochastically, starting with my current coin-based intermittent fasting and dice-based kettlebell.
Updated
May 1, 2018
Racket
Stochastic models to price financial options
Updated
Dec 7, 2020
Python
Numerical experiments with stochastic differential equations in Haskell
Updated
Dec 21, 2018
Haskell
Julia Package for Generating Scenario Trees and Scenario Lattices for Multistage Stochastic Optimization
Updated
Mar 4, 2021
Julia
🦜 DISCOTRESS 🦜 is a software package to simulate and analyse the dynamics on arbitrary Markov chains
A classic implementation in C++ of the famous 2D Ising Model.
Updated
Mar 5, 2017
Jupyter Notebook
Piecewise Deterministic Markov Processes in Julia
Updated
Jul 20, 2021
Julia
Record the learning materials of the course - "STOCHASTIC ANALYSIS OF COMPUTER NETWORKS" in National Cheng Kung University.
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I'm trying to have a multi-dimensional lengthscale for my kernel, and cannot find in the documentation how to do this. The closest I've come is specifying
input_dim, as described here, but in version 2.0.5 I get an error thatinput_dimis an unknown keyword argument. How would I get these multidimensional lengthscales in gpfl