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      Zones, spots, and planetary-scale waves beating in brown dwarf atmospheres

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          Inference from Iterative Simulation Using Multiple Sequences

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            Is Open Access

            emcee: The MCMC Hammer

            We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. 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). 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. In this document, we describe the algorithm and the details of our implementation and API. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at http://dan.iel.fm/emcee under the MIT License.
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              Thermal structure of Jupiter's atmosphere near the edge of a 5-μm hot spot in the north equatorial belt

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                Author and article information

                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                August 17 2017
                August 18 2017
                : 357
                : 6352
                : 683-687
                Article
                10.1126/science.aam9848
                6577ffc8-f79b-48d7-806f-dd0141c5c935
                © 2017

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