Estimation in the Mixture of Markov Chains Moving With Different Speeds
By Halina Frydman. 2005
53 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/64db60fe5b4667980bd2f8386fabd2549a21e32b
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
- Christoph Pamminger
- Til Schuermann
- Sylvia Frühwirth-Schnatter
- S. Kaufmann
- Anna Pajor
- Jeroen K. Vermunt
- Kerrie L. Mengersen
- Judith Rousseau
In Statistics and Probability
- Christoph Pamminger
- Til Schuermann
- Sylvia Frühwirth-Schnatter
- Halina Frydman
- S. Kaufmann
- Anna Pajor
- Jeroen K. Vermunt
- Kerrie L. Mengersen
Citations in the corpus
- Model-based clustering of categorical time series (2010, methodology): “The Bayesian approach offers several advantages in the context of Markov chain clustering compared to EM estimation as in Cadez et al. (2003) or Frydman (2005).”
- Credit Rating Dynamics and Markov Mixture Models (2008, methodology): “Thus, the distribution of a state one period later is a convex linear combinaton 3For details, see Frydman (2004). of one period transition probabilities of Markov chains.”