Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
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Updated
Jul 16, 2024 - Jupyter Notebook
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Covers the basics of mixed models, mostly using @lme4
Display and analyze ROC curves in R and S+
Project Page of Combining 3D Morphable Models: A Large scale Face-and-Head Model - [CVPR 2019]
A script toolkit for SLAM research, including but not limited to various plotting functions, ROS bag processing, and more.
I will update this repository to learn Machine learning with python with statistics content and materials
Functions for the construction of risk-based portfolios
Mixed models @lme4 + custom covariances + parameter constraints
Fast & numerically stable statistical analysis
Lightweight robust covariance estimation in Julia
SIMD-enabled descriptive statistics (mean, variance, covariance, correlation)
Kriging estimators for the GeoStats.jl framework
Dimensionality reduction on manifold of SPD matrices, based on pymanopt implementation
Gaussian process regression
Online statistics implementations, including average, variance and standard deviation; exponentially weighted versions as well.
Variography for the GeoStats.jl framework
power spectra on the masked sky
Built-in solvers for the GeoStats.jl framework
General purpose correlation and covariance estimation
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