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Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
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Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the last decade, we have also seen a steady increase in the number of papers that employ Monte Carlo based Bayesian analysis. New, efficient Monte Carlo based methods are continuously being developed and explored. In this review, we first explain the basics of Bayesian theory and discuss how to set up data analysis problems within this framework. Next, we provide an overview of various Monte Carlo based methods for performing Bayesian data analysis. Finally, we discuss advanced ideas that enable us to tackle complex problems and thus hold great promise for the future. We also distribute downloadable computer software (available at https://github.com/sanjibs/bmcmc/ ) that implements some of the algorithms and examples discussed here.
Forward citations
Cited by 6 Pith papers
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Binarity at LOw Metallicity (BLOeM): massive star variability revealed using a novel software tool for point-spread function fitting of TESS images
A PSF-fitting pipeline extracts variability light curves for 91 SMC massive stars and finds that stochastic low-frequency variability morphology tracks HR-diagram position independently of metallicity.
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Binarity at LOw Metallicity (BLOeM): massive star variability revealed using a novel software tool for point-spread function fitting of TESS images
A new PSF-fitting tool extracts TESS light curves for 91 SMC massive stars, revealing binarity, pulsations, and SLF variability whose morphology tracks HR diagram position similarly to Galactic stars.
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Gravitational-wave parameter estimation with gaps in LISA: a Bayesian data augmentation method
Bayesian data augmentation reintroduces missing LISA data segments as auxiliary variables during posterior sampling to enable consistent parameter estimation for galactic binaries despite gaps.
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Testing the reliability of magnetic field strength measurements for M dwarfs
Synthetic spectra tests show Zeeman broadening recovers M dwarf magnetic field strengths more reliably with joint abundance fitting and WAIC selection than with free line strengths or BIC/AIC, which underestimate by 3...
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A practical Bayesian method for gravitational-wave ringdown analysis with multiple modes
FIREFLY accelerates multi-mode GW ringdown analysis by analytically marginalizing QNM amplitudes and phases via Bayesian principles and importance sampling.
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Determination of the best dark matter profile for the Milky Way with Gaia DR3 using Bayesian Model Comparison
Bayesian comparison of Gaia DR3 rotation curves favors the Einasto dark matter profile over NFW, prefers cored over cuspy profiles, and finds MOND variants provide poorer fits than the best dark matter models.
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