{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hamiltonian-monte-carlo-acceleration-using","title":"Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases","arxiv_id":"1506.05555","date":"2015-06-18","proceeding":null,"authors":["Cheng Zhang","Babak Shahbaba","Hongkai Zhao"],"abstract":"For big data analysis, high computational cost for Bayesian methods often\nlimits their applications in practice. In recent years, there have been many\nattempts to improve computational efficiency of Bayesian inference. Here we\npropose an efficient and scalable computational technique for a\nstate-of-the-art Markov Chain Monte Carlo (MCMC) methods, namely, Hamiltonian\nMonte Carlo (HMC). The key idea is to explore and exploit the structure and\nregularity in parameter space for the underlying probabilistic model to\nconstruct an effective approximation of its geometric properties. To this end,\nwe build a surrogate function to approximate the target distribution using\nproperly chosen random bases and an efficient optimization process. The\nresulting method provides a flexible, scalable, and efficient sampling\nalgorithm, which converges to the correct target distribution. We show that by\nchoosing the basis functions and optimization process differently, our method\ncan be related to other approaches for the construction of surrogate functions\nsuch as generalized additive models or Gaussian process models. Experiments\nbased on simulated and real data show that our approach leads to substantially\nmore efficient sampling algorithms compared to existing state-of-the art\nmethods.","url_abs":"http://arxiv.org/abs/1506.05555v5","url_pdf":"http://arxiv.org/pdf/1506.05555v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hamiltonian-monte-carlo-acceleration-using","repo_url":"https://github.com/chengzhang-uci/RNSHMC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"additive-models","task_name":"Additive models"},{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1506.05555","atlas_url":"https://app.syntology.ai/?focus=1506.05555","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}