System uncertainty and statistical detection for jump-diffusion models

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Motivated by the common-seen model uncertainty of real-world systems, we propose a likelihood ratio-based approach to statistical detection for a rich class of partially observed systems. Here, the system state is modeled by some jump-diffusion process while the observation is of additive white noise. Our approach can be implemented recursively based on some Markov chain approximation method to compare the competing stochastic models by fitting the observed historical data. Our method is superior to the traditional hypothesis test in both theoretical and computational aspects. In particular, a wide range of different models can be nested and compared in a unified framework with the help of Bayes factor. An illustrating numerical example is also given to show the application of our method.
Original languageEnglish
Article number5393008
Pages (from-to)697-702
Number of pages6
JournalIEEE Transactions on Automatic Control
Issue number3
Publication statusPublished - 1 Mar 2010


  • Bayes factor
  • Jump-diffusion process
  • Markov chain approximation
  • System uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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