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Geometric Stick-Breaking Processes for Continuous-Time Nonparametric Modeling

Author

Listed:
  • Ramses H. Mena

    ()

  • Matteo Ruggiero
  • Stephen G. Walker

Abstract

This paper is concerned with the construction of a continuous parameter sequence of random probability measures and its application for modeling random phenomena evolving in continuous time. At each time point we have a random probability measure which is generated by a Bayesian nonparametric hierarchical model, and the dependence structure is induced through a Wright-Fisher diffusion with mutation. The sequence is shown to be a stationary and reversible diffusion taking values on the space of probability measures. A simple estimation procedure for discretely observed data is presented and illustrated with simulated and real data sets.

Suggested Citation

  • Ramses H. Mena & Matteo Ruggiero & Stephen G. Walker, 2009. "Geometric Stick-Breaking Processes for Continuous-Time Nonparametric Modeling," ICER Working Papers - Applied Mathematics Series 26-2009, ICER - International Centre for Economic Research.
  • Handle: RePEc:icr:wpmath:26-2009
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    Keywords

    Bayesian non-parametric inference; continuous time dependent random measure; Markov process; measure-valued process; stationary process; stick-breaking process;

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