Inference and temporal recovery in grown trees
Python implementation of efficient recovery algorithms for grown trees, introduced in "Inference, Model Selection, and the Combinatorics of Growing Trees".
temporal_recovery.pycontains our algorithms for recovering the history of a tree.
boundary_sampler.pyis a Python implementation of the data structure described in section 3.2.2 of "Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm"
full_marginal.pycontains code to compute the exact one-node marginals.
- A C++/Python version of this code is available at https://github.com/gstonge/fasttr
If you use this code, please consider citing: