HOPS_tk

HOPS_tk implements efficient and scalable Markov Chain Monte Carlo (MCMC) sampling within convex-constrained spaces, supporting both uniform and non-uniform (arbitrary target function) targets for statistical inference and optimization.

Key Features:

• Implementation: Implemented as an open-source C++17 library providing high-performance MCMC sampling routines.
• Efficient Uniform Sampling: Achieves substantial performance improvements in uniform sampling compared to existing state-of-the-art methods.
• Non-Uniform Sampling: Supports non-uniform sampling and arbitrary target functions for complex probabilistic models and Bayesian inference.
• Convex-Constrained Sampling: Specialized for sampling within convex-constrained spaces encountered in optimization and statistical problems.
• Interoperability: Integrates with third-party software to support diverse computational workflows.
• Scalability: Designed to handle large-scale computations suitable for high-performance computing environments.

Scientific Applications:

• Bayesian Inference: Applied for posterior sampling and statistical modeling in Bayesian inference workflows.
• Convex-Constrained Models: Used for sampling and analysis in optimization problems and machine learning models constrained to convex domains.

Methodology:

Utilizes advanced MCMC techniques to ensure efficient exploration of the sample space. Designed to handle large-scale computations for high-performance environments.