Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method

By Christian P. Robert, Tobias Rydén, D. M. Titterington. 2000

225 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/01894b1d8b41299dd887ba9f20530c6f8c9f1ac3

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. Christian P. Robert
2. Arnaud Doucet
3. Ajay Jasra
4. Olivier Capp'e
5. Peter J. Green
6. Christopher C. Holmes
7. Nicolas Chopin
8. Tobias Rydén

In Finance

1. Benjamin Renard
2. Matthew Lang
3. Taha B. M. J. Ouarda
4. S. El Adlouni
5. Wolfgang Karl Härdle
6. Peter J. Green
7. C. Fernández
8. Olivier Capp'e

In Statistics and Probability

1. Arnaud Doucet
2. Christian P. Robert
3. Peter J. Green
4. Christopher C. Holmes
5. Ajay Jasra
6. Tobias Rydén
7. Nicolas Chopin
8. Jaco Vermaak

Citations in the corpus

• Reversible Jump Markov Chain Monte Carlo Strategies for Bayesian Model Selection in Autoregressive Processes (2004, background): “…Bayesian models have been 0143-9782/04/06 785–809 JOURNAL OF TIME SERIES ANALYSIS Vol. 25, No. 6 2004 Blackwell Publishing Ltd., 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. developed in different contexts in (Richardson and Green, 1997; Robert et al., 2000).”
• Dealing with label switching in mixture models (2000, background): “Robert et al. (2000)) the likelihood is invariant under permutation of the labels of the hidden states.”
• Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling (2005, methodology): “Stephens (2000a) used a continuous time sampler to draw from a bivariate mixture posterior.”
• Joint Bayesian model selection and parameter estimation of the generalized extreme value model with covariates using birth‐death Markov chain Monte Carlo (2009, methodology): “These methods are largely used for mixtures of distributions with an unknown number of components [Richardson and Green, 1997; Robert et al., 2000; Dellaportas and Papageorgiou, 2006].”
• Statistical analysis of extreme events in a non-stationary context via a Bayesian framework: case study with peak-over-threshold data (2006, methodology): “In the step-change case, Hidden Markov Chains can be used for this purpose ( Robert et al. 2000; Perreault and Fortin 2003).”
• Modelling spatially correlated data via mixtures: a Bayesian approach (2002, methodology): “…al., 1994), measurement error (Müller and Roeder, 1997; Leblond et al., 2000), modelling random effects in mixed models (Watier et al., 1999), analysis of factorial experiments (Nobile and Green, 2000) and, more generally, hidden switching models (Robert et al., 2000; FrühwirthSchnatter, 2001).”
• A generalized ARFIMA process with Markov-switching fractional differencing parameter (2009, background): “differencing parameter and the process level has been considered in Ray and Tsay (2002) .”
• Reversible jump, birth‐and‐death and more general continuous time Markov chain Monte Carlo samplers (2003, methodology): “The log-likelihoods cover a wider range than those found in Robert et al. (2000), although the highest values are the same.”