scholarly journals Detecting planetary signals with Bayesian methods

2009 ◽  
Vol 5 (H15) ◽  
pp. 693-693
Author(s):  
Samuli Kotiranta ◽  
Mikko Tuomi

AbstractIn this paper we present an application of Bayesian model comparison to the radial velocity measurements of suspected extra-solar planetary system host star.

2021 ◽  
Vol 654 ◽  
pp. A104
Author(s):  
N. Unger ◽  
D. Ségransan ◽  
D. Queloz ◽  
S. Udry ◽  
C. Lovis ◽  
...  

Context. We present precise radial-velocity measurements of five solar-type stars observed with the HARPS Echelle spectrograph mounted on the 3.6-m telescope in La Silla (ESO, Chile). With a time span of more than 10 yr and a fairly dense sampling, the survey is sensitive to low mass planets down to super-Earths on orbital periods up to 100 days. Aims. Our goal was to search for planetary companions around the stars HD 39194, HD 93385, HD 96700, HD 154088, and HD 189567 and use Bayesian model comparison to make an informed choice on the number of planets present in the systems based on the radial velocity observations. These findings will contribute to the pool of known exoplanets and better constrain their orbital parameters. Methods. A first analysis was performed using the Data & Analysis Center for Exoplanets online tools to assess the activity level of the star and the potential planetary content of each system. We then used Bayesian model comparison on all targets to get a robust estimate on the number of planets per star. We did this using the nested sampling algorithm POLYCHORD. For some targets, we also compared different noise models to disentangle planetary signatures from stellar activity. Lastly, we ran an efficient Markov chain Monte Carlo algorithm for each target to get reliable estimates for the planets’ orbital parameters. Results. We identify 12 planets within several multiplanet systems. These planets are all in the super-Earth and sub-Neptune mass regime with minimum masses ranging between 4 and 13 M⊕ and orbital periods between 5 and 103 days. Three of these planets are new, namely HD 93385 b, HD 96700 c, and HD 189567 c.


2014 ◽  
pp. 101-117
Author(s):  
Michael D. Lee ◽  
Eric-Jan Wagenmakers

2018 ◽  
Vol 265 ◽  
pp. 271-278 ◽  
Author(s):  
Tyler B. Grove ◽  
Beier Yao ◽  
Savanna A. Mueller ◽  
Merranda McLaughlin ◽  
Vicki L. Ellingrod ◽  
...  

2021 ◽  
Author(s):  
John K. Kruschke

In most applications of Bayesian model comparison or Bayesian hypothesis testing, the results are reported in terms of the Bayes factor only, not in terms of the posterior probabilities of the models. Posterior model probabilities are not reported because researchers are reluctant to declare prior model probabilities, which in turn stems from uncertainty in the prior. Fortunately, Bayesian formalisms are designed to embrace prior uncertainty, not ignore it. This article provides a novel derivation of the posterior distribution of model probability, and shows many examples. The posterior distribution is useful for making decisions taking into account the uncertainty of the posterior model probability. Benchmark Bayes factors are provided for a spectrum of priors on model probability. R code is posted at https://osf.io/36527/. This framework and tools will improve interpretation and usefulness of Bayes factors in all their applications.


2017 ◽  
Vol 70 ◽  
pp. 84-93 ◽  
Author(s):  
R. Wesley Henderson ◽  
Paul M. Goggans ◽  
Lei Cao

2018 ◽  
Author(s):  
Julia M. Haaf ◽  
Fayette Klaassen ◽  
Jeffrey Rouder

Most theories in the social sciences are verbal and provide ordinal-level predictions for data. For example, a theory might predict that performance is better in one condition than another, but not by how much. One way of gaining additional specificity is to posit many ordinal constraints that hold simultaneously. For example a theory might predict an effect in one condition, a larger effect in another, and none in a third. We show how common theoretical positions naturally lead to multiple ordinal constraints. To assess whether multiple ordinal constraints hold in data, we adopt a Bayesian model comparison approach. The result is an inferential system that is custom-tuned for the way social scientists conceptualize theory, and that is more intuitive and informative than current linear-model approaches.


2021 ◽  
Vol 84 (1) ◽  
Author(s):  
S. Pasetto ◽  
H. Enderling ◽  
R. A. Gatenby ◽  
R. Brady-Nicholls

AbstractThe prostate is an exocrine gland of the male reproductive system dependent on androgens (testosterone and dihydrotestosterone) for development and maintenance. First-line therapy for prostate cancer includes androgen deprivation therapy (ADT), depriving both the normal and malignant prostate cells of androgens required for proliferation and survival. A significant problem with continuous ADT at the maximum tolerable dose is the insurgence of cancer cell resistance. In recent years, intermittent ADT has been proposed as an alternative to continuous ADT, limiting toxicities and delaying time-to-progression. Several mathematical models with different biological resistance mechanisms have been considered to simulate intermittent ADT response dynamics. We present a comparison between 13 of these intermittent dynamical models and assess their ability to describe prostate-specific antigen (PSA) dynamics. The models are calibrated to longitudinal PSA data from the Canadian Prospective Phase II Trial of intermittent ADT for locally advanced prostate cancer. We perform Bayesian inference and model analysis over the models’ space of parameters on- and off-treatment to determine each model’s strength and weakness in describing the patient-specific PSA dynamics. Additionally, we carry out a classical Bayesian model comparison on the models’ evidence to determine the models with the highest likelihood to simulate the clinically observed dynamics. Our analysis identifies several models with critical abilities to disentangle between relapsing and not relapsing patients, together with parameter intervals where the critical points’ basin of attraction might be exploited for clinical purposes. Finally, within the Bayesian model comparison framework, we identify the most compelling models in the description of the clinical data.


Sign in / Sign up

Export Citation Format

Share Document