Probability Theory: Author index
By E. Jaynes, G. Bretthorst. 2003
535 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
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
- Hervé Philippe
- Nicolas Lartillot
- Jürgen Landes
- Rubens Sampaio
- M. B. Rosales
- Cees G. M. Snoek
- T. Ritto
- Marcel Worring
In Statistics and Probability
- James L. Beck
- Ka-Veng Yuen
- Rubens Sampaio
- T. Ritto
- Jürgen Landes
- M. B. Rosales
- Cees G. M. Snoek
- Marcel Worring
Citations in the corpus
- Information and Entropy Econometrics - A Review and Synthesis (2008, background): “See also Jaynes (1984) and his nice text (Jaynes, 2003) for additional discussion.”
- Statistical mechanics of ecological systems: Neutral theory and beyond (2015, methodology): “The maximum entropy principle is a useful method to obtain the least biased information from empirical measurements [80].”
- Probabilism, entropies and strictly proper scoring rules (2015, background): “613 MaxEnt has given rise to a substantial literature on rational belief for614 mation; as examples we mention [1, 8, 23, 29, 40, 41].”
- Computing Bayes factors using thermodynamic integration. (2006, background): “The simple and intuitive interpretation of the concept of probabilities underlying the Bayesian paradigm makes it an appealing framework of scienti(cid:12)c inference in general (Jaynes, 2003).”
- Applications of Bayesian model selection to cosmological parameters (2005, methodology): “In this section, we first briefly review the basics of Bayesian inference and model comparison and introduce our notation.”
- Model Selection using Response Measurements: Bayesian Probabilistic Approach (2004, methodology): “The uncertainties in the values of the model parameters can be updated using Bayesian inference ͑Jeffreys 1961; Box and Tiao 1973; Sivia 1996; Jaynes 2003͒.”
- A high-bias, low-variance introduction to Machine Learning for physicists (2018, unknown): “We mention in passing that the MaxEnt in ML is also closely related to ideas from Bayesian inference (Jaynes, 1968, 2003) and this latter point of view is more common in discussions of MaxEnt in the statistics and ML literature.”
- Gravitational wave astronomy of single sources with a pulsar timing array (2011, unknown): “Due to this assumption, the CRB is not applicable to cases, where the UBE does not exist (Jaynes 2003).”