2012
DOI: 10.1088/0004-637x/745/2/198
|View full text |Cite
|
Sign up to set email alerts
|

An Affine-Invariant Sampler for Exoplanet Fitting and Discovery in Radial Velocity Data

Abstract: Markov Chain Monte Carlo (MCMC) proves to be powerful for Bayesian inference and in particular for exoplanet radial velocity fitting because MCMC provides more statistical information and makes better use of data than common approaches like chisquare fitting. However, the non-linear density functions encountered in these problems can make MCMC time-consuming. In this paper, we apply an ensemble sampler respecting affine invariance to orbital parameter extraction from radial velocity data. This new sampler has … Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
55
0

Year Published

2012
2012
2024
2024

Publication Types

Select...
9
1

Relationship

1
9

Authors

Journals

citations
Cited by 72 publications
(55 citation statements)
references
References 28 publications
0
55
0
Order By: Relevance
“…The individual thermal observations were obtained when the object was at different heliocentric and geocentric distances, so, although the differences are small, each flux density is modeled for the individual distance that the observation was made at. We use the Python package emcee (Foreman-Mackey et al 2013), which provides a convenient and parallel implementation of the Hou et al (2012) affine invariant ensemble sampler for MCMC. In each of the modeling cases that we discuss below, we assign uniform priors to the parameters and run an ensemble of 100 chains through 10 4 steps after a 10 3 step initialization ("burn-in") period.…”
Section: Multi-parameter Markov Chain Monte Carlo Thermal Modelingmentioning
confidence: 99%
“…The individual thermal observations were obtained when the object was at different heliocentric and geocentric distances, so, although the differences are small, each flux density is modeled for the individual distance that the observation was made at. We use the Python package emcee (Foreman-Mackey et al 2013), which provides a convenient and parallel implementation of the Hou et al (2012) affine invariant ensemble sampler for MCMC. In each of the modeling cases that we discuss below, we assign uniform priors to the parameters and run an ensemble of 100 chains through 10 4 steps after a 10 3 step initialization ("burn-in") period.…”
Section: Multi-parameter Markov Chain Monte Carlo Thermal Modelingmentioning
confidence: 99%
“…We display the results both as a PDF and a cumulative density function (CDF) fit to the data (see Figure 8). The plotted 11 We perform the sampling with a modified C version of the original Python implementation (Foreman-Mackey et al 2013) of an affine-invariant ensemble sampler (Hou et al 2012) using an ensemble of 1000 walkers. with the best fit model PDF (solid red line) surrounded by a ±1σ error region (gray shading).…”
Section: Black Hole Mass Function For Local Spiral Galaxiesmentioning
confidence: 99%
“…The posterior is approximated using the affine invariant MCMC sampler emcee (Foreman-Mackey et al 2013; see also Hou et al 2012). Using the emcee routine, the posterior distribution is mapped, after which parameter estimates are evaluated as the median of the respective marginalised distributions (see Sect.…”
Section: A1 Modelling the Power Spectrummentioning
confidence: 99%