Bayesian Logical Data Analysis for the Physical Sciences: Probability theory as extended logic

By P. Gregory. 2005

261 citations recorded by the source corpus.

Subfields: Artificial Intelligence, History and Philosophy of Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3f62dcf6dd4a41f78c8ee3dbc44657ddffa800da

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David W. Hogg
2. M. Beuthe
3. Daniel Foreman-Mackey
4. Dustin Lang
5. Jonathan B. Goodman
6. J. Casares
7. Tariq Shahbaz
8. A. Trinh

In History and Philosophy of Science

1. M. Beuthe
2. Daniel Foreman-Mackey
3. Dustin Lang
4. Jonathan B. Goodman
5. David W. Hogg
6. A. Trinh
7. A. Rivoldini

In Statistics and Probability

1. Daniel Foreman-Mackey
2. David W. Hogg
3. M. Beuthe
4. Dustin Lang
5. Jonathan B. Goodman
6. J. Casares
7. Tariq Shahbaz
8. A. Trinh

Citations in the corpus

• emcee: The MCMC Hammer (2012, methodology): “—The simplest and most commonly used MCMC algorithm is the M–H method (Algorithm 1; MacKay 2003; Gregory 2005; Press et al. 2007; Hogg et al. 2010).”
• Enceladus's and Dione's floating ice shells supported by minimum stress isostasy (2016, methodology): “, the conditional probability for the parameters given the data [Tarantola, 2005; Gregory, 2005].”
• Peering into the Dark Side: Magnesium Lines Establish a Massive Neutron Star in PSR J2215+5135 (2018, background): “1×10 21 cm(Gentile et al. 2014) using the conversion from Predehl & Schmitt (1995).”