BAT.jl -- A Julia-based tool for Bayesian inference
作者： Frederik Beaujean , Kevin Kröninger , Cornelius Grunwald , Salvatore La Cagnina , Vasyl Hafych
DOI:
关键词: Statistical inference 、 Bayesian probability 、 Development (topology) 、 Realm 、 Test suite 、 Software 、 Machine learning 、 Artificial intelligence 、 Bayesian inference
摘要: We describe the development of a multi-purpose software for Bayesian statistical inference, BAT.jl, written in Julia language. The major design considerations and implemented algorithms are summarized here, together with test suite that ensures proper functioning algorithms. also give an extended example from realm physics demonstrates functionalities BAT.jl.
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