This is the Windows app named DynamicHMC whose latest release can be downloaded as v3.6.0sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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DynamicHMC
DESCRIPTION
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
Programming Language
Julia
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/dynamichmc.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
