Gaussian processes in TensorFlow

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

Python framework for short-term ensemble prediction systems.

Generate realizations of stochastic processes in python.

📦 Python library for Stochastic Processes Simulation and Visualisation

EasyTPP: Towards Open Benchmarking Temporal Point Processes

Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston

Multifractal Detrended Fluctuation Analysis in Python

Economic scenario generator for python: simulate stocks, interest rates, and other stochastic processes.

Language modeling via stochastic processes. Oral @ ICLR 2022.

This repository contains the source code for "Stochastic data-driven model predictive control using Gaussian processes" (SDD-GP-MPC).

This repository contains the material (datasets, code, videos, spreadsheets) related to my book Stochastic Processes and Simulations - A Machine Learning Perspective.

JumpDiff: Non-parametric estimator for Jump-diffusion processes for Python

PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)

PyCurve : Python Yield Curve is a package created in order to interpolate yield curve, create parameterized curve and create stochastic simulation.

My book: Gentle Introduction to Chaotic Dynamical Systems. Includes stochastic dynamical systems and statistical properties of numeration systems in any dimension.

SdePy: Numerical Integration of Ito Stochastic Differential Equations

Predicting stock prices using Geometric Brownian Motion and the Monte Carlo method

Adaptive control for skid-steer robots using GP-enhanced MPPI for robust navigation and obstacle avoidance on diverse terrains.

Stochastic models to price financial options