NMA Computational Neuroscience course
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Updated
May 23, 2024 - Jupyter Notebook
NMA Computational Neuroscience course
Tools for Stochastic Simulation using diffusion models (R).
R package for statistical inference using partially observed Markov processes
Rust library for quantitative finance.
📦 Python library for Stochastic Processes Simulation and Visualisation
Gaussian process explorations
A queueing package for GNU Octave
DVFS framework
Gaussian processes in TensorFlow
This repository contains work for Stefano's first thesis chapter, which estimates how mean resources and the variace around them affect animals' spatial needs.
Python framework for short-term ensemble prediction systems.
Replication of the research paper : "Discretization of continuous-time arbitrage strategies in financial markets with fBm""
EasyTPP: Towards Open Benchmarking Temporal Point Processes
Suite of simulations of spatio-temporal early warning signals of stochastic and deterministic dynamical systems
Piecewise Deterministic Markov Processes in Julia
Will be a Gaussian Process library, aiming to cover regression, classification and latent space modelling.
Repository of 2 projects related to stochastic signals. First one focus on basics of signals. Second one focus on building a finite causal FIR Weiner Filter for noise cancellation.
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
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