fire_limitation

Bayesian parameterisation of the Kelley et al. global fire limitation model.

how to optimize

Optimization is performed in two jupter notebooks in "notebooks" dir:

• notebooks/prepare_data.ipynb prepares data ready for optimization.
• notebooks/bayesian_inference.ipynb performs the optimzation, and can be used without prepare_data if using csv input data.

what to install

To run the optimation, you first need jupyter installed (see here), along withthe following libraries:

• numpy as
• pandas
• pymc3
• scipy
• theano

netCDF4 will also be required to run prepare_data, and matplotlib to perform plotting.

Reproducing "How contemporary bioclimatic and human controls change global fire regimes"

To run inference used in this paper, use the paper1_bayes branch. To run with the original data, please contact douglas.i.kelley@gmail.com.