Oficial GitHub repo: https://github.com/ja-vazquez/SimpleMC
Docs: https://igomezv.github.io/SimpleMC/index.html#
- A computer, Internet conection and a Google account (to use google colab).
- Otherwise, you must install the dependecies from requirements_full.txt. You can use pip3 install -r requirements_full.txt.
For didactic purposes, we recommend Google Colab, a free Google service to run notebooks in the cloud, allowing to use Python 2 or Python 3 with CPU, GPU and TPU. Only a Google account is required. SimpleMC is coded in Python 3.
You can find this repo from Google Colab, open it and make a copy to save your changes to Google Drive. You also can clone or download this repository and run the notebooks locally or in Google Colab.
- Overview of Bayesian inference and parameter estimation
- Introduction and installation of SimpleMC.
- First examples in SimpleMC:
- CosmoCalc
- Optimizers.
- Review for Day 1.
- Bayesian inference.
- Using an ini file (exercise).
- Model comparison (exercise).
- New models.
- Without cosmology.
- Based on LCDM.
- New datasets (optional, exercise).