Comparing distributions by using dependent normalized random‐measure mixtures
By Jim E. Griffin, M. Kolossiatis, Mark F. J. Steel. 2013
54 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1846a7b03b0c5de8a3b6241a83408e199cd13490
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
- Alejandro Jara
- Fernando A. Quintana
- S. MacEachern
- Peter Mueller
- Jim E. Griffin
- Fabrizio Leisen
- David B. Dunson
- Athanasios Kottas
In Statistics and Probability
- Alejandro Jara
- S. MacEachern
- Fernando A. Quintana
- Peter Mueller
- Jim E. Griffin
- Fabrizio Leisen
- David B. Dunson
- Athanasios Kottas
Citations in the corpus
- Compound random measures and their use in Bayesian non‐parametrics (2014, methodology): “ F-distribution with ˚and ˚degrees of freedom. 5.1 Links to other processes Corollary4.1shows how the priors described in [23], [24] and [39] can be expressed in the CoRM framework. The CNMRI process [16][see also27,3] can also be expressed in the NCoRM framework. The CNMRI prior express the 15 rand”
- The Dependent Dirichlet Process and Related Models (2020, methodology): “A clever variation of this construction is introduced in Kolossiatis, Griffin and Steel (2013) who chose the weight to ensure that Gj remains marginally a DP again.”