Describe a usecase of the solver
Wait time distributions between events can follow non-Poisson distributions in reality. This change can have a significant impact on the dynamics of a system.
Describe alternatives you've considered (optional)
The Laplace Gillespie is more efficient, however the details to implement are unclear and not all distributions can be simulated that algorithm.
Describe changes to pyRBM core code that might be required to implement (optional)
As like the Laplace Gillespie, a system to authenticate distribution parameters and return a sampling function. Additionally a field to specify a distribution and its parameters for a rule.
Provide the solver name Non Markovian Gillespie Algorithm (nMGA)
Link a paper or provide a reference for the described solver https://arxiv.org/abs/1310.0926
Describe a usecase of the solver Wait time distributions between events can follow non-Poisson distributions in reality. This change can have a significant impact on the dynamics of a system.
Describe alternatives you've considered (optional) The Laplace Gillespie is more efficient, however the details to implement are unclear and not all distributions can be simulated that algorithm.
Describe changes to pyRBM core code that might be required to implement (optional) As like the Laplace Gillespie, a system to authenticate distribution parameters and return a sampling function. Additionally a field to specify a distribution and its parameters for a rule.