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Implementation of robust dynamic Hamiltonian Monte Carlo methods in Julia. In contrast to frameworks that utilize a directed acyclic graph to build a posterior for a Bayesian model from small components, this package requires that you code a log-density function of the posterior in Julia. Derivatives can be provided manually, or using automatic differentiation. Consequently, this package requires that the user is comfortable with the basics of the theory of Bayesian inference, to the extent of coding a (log) posterior density in Julia. This approach allows the use of standard tools like profiling and benchmarking to optimize its performance.
Features
- The building blocks of the algorithm are implemented using a functional (non-modifying) approach whenever possible
- Examples available
- Derivatives can be provided manually, or using automatic differentiation
- Robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia
- Modern version of the “No-U-turn sampler” in the Julia language
- Standard tools like profiling and benchmarking to optimize its performance
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Data VisualizationLicense
MIT LicenseFollow DynamicHMC
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