Thomas Bayes’ walk on manifolds
By I. Castillo, Gerard Kerkyacharian, Dominique Picard. 2012
53 citations recorded by the source corpus.
Convergence of the Bayes posterior measure is considered in canonical statistical settings where observations sit on a geometrical object such as a compact manifold, or more generally on a compact metric space verifying some conditions. A natural geometric prior based on randomly rescaled solutions of the heat equation is considered. Upper and lower bound posterior contraction rates are derived.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/0497dc8043bd13df457f8cc9a459ef185358ee58
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
- Johannes Schmidt-Hieber
- Judith Rousseau
- Marc Hoffmann
- I. Castillo
- Kolyan Ray
- Richard Nickl
- A. van der Vaart
- Taiji Suzuki
In Statistical and Nonlinear Physics
In Statistics and Probability
- Johannes Schmidt-Hieber
- Judith Rousseau
- Marc Hoffmann
- I. Castillo
- Richard Nickl
- Kolyan Ray
- A. van der Vaart
- Botond Szab'o
Citations in the corpus
- On adaptive posterior concentration rates (2013, methodology): “sal [3]), Markov models (Tang and Ghosal [29]), Gaussian time series (Choudhuri et al. [11] and Rousseau et al. [25]) to name but a few, or the recent canonical statistical setting of Castillo et al. [10]. For each of these models a variety of families of priors have been investigated. An interestin”