Convergence Controls for MCMC Algorithms with Applications to Hidden Markov Chains

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Effective Bayesian Modeling of Groups of Related Count Time Series (2014, methodology): “3) all have the same meaning as previously, except that the ` index makes them dependent on a specific time series.”
Bayesian Methods for Hidden Markov Models (2002, methodology): “Figure 13( Appropriate diagnostics for model adequacy will depend on the application at hand, but two generally useful diagnostics are the autocorrelation function (ACF), advocated by MacDonald and Zucchini (1997), and the predictive distribution of the data, advocated by Robert et al. (1999).”
Bayesian Modelling and Inference on Mixtures of Distributions (2005, methodology): “Example 4 This example deals with a benchmark of mixture estimation, the galaxy dataset of Roeder (1992), also analyzed in Richardson and Green (1997) and Roeder and Wasserman (1997), among others.”
Reversible jump, birth‐and‐death and more general continuous time Markov chain Monte Carlo samplers (2003, methodology): “This can be avoided because of both the forward recursive representation of the likelihood for a hidden Markov model (Baum et al., 1970), which has been used before in Robert et al. (1999), and the random-walk proposals as in Hurn et al. (2003).”