Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support
By P. Gregory. 2005
138 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/a3ae59c919c1b2b1f88a1fc28dd428660b5e5426
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Citations in the corpus
- γ2 Velorum: orbital solution and fundamental parameter determination with SUSI (2007, methodology): “The final uncertainty estimation method involves a likelihood based random walk through parameter space using a Markov chain Monte Carlo (MCMC) simulation implemented with a Metropolis-Hastings algorithm (see Chapter 12 of Gregory 2005a for an introduction to MCMC).”
- An introduction to Bayesian inference in gravitational-wave astronomy: Parameter estimation, model selection, and hierarchical models (2018, background): “Sivia & Skilling (2006) and Gregory (2005) are useful references that are accessible to physicists and astronomers; see also the Springer Series in Astrostatistics (Manuel et al., 2012; Hilbe, 2013; Chattopadhyay & Chattopadhyay, 2014; Andreon & Weaver, 2015).”
- Retrieving Temperatures and Abundances of Exoplanet Atmospheres with High-resolution Cross-correlation Spectroscopy (2018, background): “significantly improve precisions on the aforementioned quantities (Greene et al. 2016), due to its extremely broad, continuous (0.”
- Systemic: A Testbed for Characterizing the Detection of Extrasolar Planets. I. The Systemic Console Package (2009, methodology): “More sophisticated Bayesian algorithms, such as parallel tempering MCMC (Gregory 2005a), may be implemented by the user by exploiting the programmable interface of the Console.”
- Peering into the Dark Side: Magnesium Lines Establish a Massive Neutron Star in PSR J2215+5135 (2018, unknown): “We use a Bayesian framework to determine our binary model parameters (Gregory 2005).”
- TERRESTRIAL PLANET OCCURRENCE RATES FOR THE KEPLER GK DWARF SAMPLE (2015, methodology): “We extend the method of Youdin (2011) by employing Bayesian parameter estimation theory using Markov Chain Monte-Carlo (MCMC) methods to numerically evaluate the posterior distribution of the PLDF parameters (Gregory 2005).”