Comparison of several Regression techniques and Gaussian Process.
Examples taken from:
[1] MARTIN, OSVALDO. Bayesian Analysis with Python -: Implement Statistical Modeling and Probabilistic Programming Using pymc3. PACKT Publishing Limited, 2018.
- Linear regression
- Robust linear regression
- Logistic regression
- Multivariate Linear and Logistic regression
- Poisson regression (ZIP)
- Polynomial regression (univariate and multivariate)
- Linear splines
- Gaussian Process Regression
- Regression with spatial autocorrelation
- Gaussian Process Classification
- GP Classification with a More Complex Target
- Poisson Process (and Cox Process)
.
├── data # Datasets used in notebooks
├── world # Geodata for plotting world maps
├── 1.* ... 5.*.ipynb # Notebooks
├── guassian_processes.py # GP utility class
├── utils.py # Utility functions
├── LICENSE
└── README.md
Recommended to create a venv; recommended to install pymc separatly with conda if on windows (Instructions);
then:
pip install -r requirements.txt
Just use them as a regular ipy notebooks