![]() SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. The code is available online at this http URL under the MIT License.Ībstract: SciPy is an open source scientific computing library for the Python programming language. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. In this document, we describe the algorithm and the details of our implementation and API. One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to $\sim N^2$ for a traditional algorithm in an N-dimensional parameter space. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). The code is open source and has already been used in several published projects in the astrophysics literature. TL DR: This document introduces a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman
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