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Hamiltonian Monte Carlo relies on gradient information to make smarter move proposals than Metropolis Hastings.
This puts it somewhat outside the current focus of PyBNF, which is problems for which gradient information is not readily available.
Potential alternative to MH, supposed to be better.
A variant is the "NUTS" algorithm, which is implemented in the package "stan"
Should look into at some point.
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