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Keywords:

  • Cox–Ingersoll–Ross model;
  • EM algorithm;
  • Graphical models;
  • Markov chain Monte Carlo methods;
  • Monte Carlo maximum likelihood;
  • Retrospective sampling

Summary.  The objective of the paper is to present a novel methodology for likelihood-based inference for discretely observed diffusions. We propose Monte Carlo methods, which build on recent advances on the exact simulation of diffusions, for performing maximum likelihood and Bayesian estimation.