A selection of Jupyter notebooks
These are notebooks using our code to verify that we can come to the same conclusions as those found in the literature our work is based upon. (Typically checking that optimisation schemes can find parameters from data simulated directly from the same statistical model, for example.)
- Self-excited point process: Using the "stocastic declustering algorithm" to recreate the appendix from Mohler et al (2011)
- Self-excited point process 2: Using an Expectation Maximisation algorithm to recreate the first part of Lewis and Mohler (2011)
- Self-excited point process 2a: Further exploration of the EM algorithm, focusing on sample size and parameter range
- Kernel estimation: A look at some of the kernel estimation techniques we need