Bayesian inference for discretely observed CTMCs via a pseudo-likelihood that couples the transition matrix to a spectral generator, with a Gibbs sampler whose cost per iteration is near-invariant in the number of observations.
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Efficient Bayesian Inference for Discretely Observed Continuous Time Markov Chains
Bayesian inference for discretely observed CTMCs via a pseudo-likelihood that couples the transition matrix to a spectral generator, with a Gibbs sampler whose cost per iteration is near-invariant in the number of observations.