Monte-carlo-simulation Libraries

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Monte-carlo-simulation Libraries

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

Here you find my IPython (Jupyter) notebooks from my blog (ipythonquant.wordpress.org). Feel free to download the samples and play around with them.

GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. It also offers support for stochastic modeling to address parameter and model uncertainties.

Welcome to the era of GemPy v3! We are thrilled to announce the release of the latest version, a product of meticulous planning, redesign, and rigorous testing. While the core essence remains intact,…

GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. It also offers support for stochastic modeling to address parameter and model uncertainties.

Open-source, implicit 3D structural geological modeling in Python. We provide the latest release version of GemPy via PyPi package services. We highly recommend using PyPi, The following versions are…

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

To engage in conversations about uncertainty quantification, or ask question about UQpy usage and functionality refer to the UQpy's discussions tab: UQpy (Uncertainty Quantification with python) is a…

ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.

ParaMonte has been developed while bearing the following design goals in mind: The ParaMonte library relies on three independent parallelism paradigms for parallel applications: The ParaMonte librari…

AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

Thank you for reading and for being a supporter of Towards AI! Big thank you to C4H3I LLC for sponsoring us in June, 2022!

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

Required Files: Linear regression is a linear approach to model the relationship between a scalar response (dependent varaible) and one or more explanatory variables (independent variables). The case…

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