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<tt>emcee</tt>: The MCMC Hammer
Open accessAbstract
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 ∼ 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. 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 GNU General Public License v2.
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Gregory, P. (2005) · doi:10.1017/cbo9780511791277 · Cited by 750
THE MILKY WAY'S CIRCULAR-VELOCITY CURVE BETWEEN 4 AND 14 kpc FROM APOGEE DATA
Bovy, J., Allende Prieto, C., Beers, T. C., Bizyaev, D., da Costa, L. N., Cunha, K., Ebelke, G. L., Eisenstein, D. J., Frinchaboy, P. M., García Pérez, A. E., et al. (2012) · The Astrophysical Journal · doi:10.1088/0004-637x/759/2/131 · Cited by 413
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Cited by
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
Riess, A. G., Yuan, W., Macri, L. M., Scolnic, D., Brout, D., Casertano, S., Jones, D. O., Murakami, Y., Anand, G. S., Breuval, L., et al. (2022) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ac5c5b · Cited by 2,380
UniverseMachine: The correlation between galaxy growth and dark matter halo assembly from z = 0−10
Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1182 · Cited by 1,243
Dark Energy Survey year 1 results: Cosmological constraints from galaxy clustering and weak lensing
Abbott, T. M. C., Abdalla, F. B., Alarcon, A., Aleksić, J., Allam, S., Allen, S., Amara, A., Annis, J., Asorey, J., Avila, S., et al. (2018) · Physical Review D · doi:10.1103/physrevd.98.043526 · Cited by 1,213
Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and weak lensing
Abbott, T. M. C., Aguena, M., Alarcon, A., Allam, S., Alves, O., Amon, A., Andrade-Oliveira, F., Annis, J., Avila, S., Bacon, D., et al. (2022) · Physical Review D · doi:10.1103/physrevd.105.023520 · Cited by 1,124
Metadata
- Authors
- Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J.
- Venue
- Publications of the Astronomical Society of the Pacific
- Publisher
- IOP Publishing
- Published
- 2013-03
- Volume
- 125
- Issue
- 925
- Pages
- 306-312
- ISSN
- 0004-6280 (ISSN-L)
- 1538-3873
- OpenAlex
- W2063397409
- Cited by
- 12,414
- References
- 2
- FWCI
- 213.33
- Type
- journal article
- Language
- en
- Open access
- bronze
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