Markov jump-diffusion models and decision-making-free filtering

  • H. A. P. Blom
Session 10 Filtering
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 62)


The problem considered is non-linear filtering of Gaussian observations of a Markov jump-diffusion with an embedded Markov chain, that is described by stochastic differential equations driven by Brownian motions and a random Poisson measure. The modelling potential of this class of Markov processes is illustrated by some simple realistic examples.

For the evolution of the conditional expectation of the Markov process decomposed representations are given. They are used as a basis to obtain approximate filtering algorithms that are free of decision making mechanisms. These algorithms are discussed for the examples given.


Markov Chain Markov Process Stochastic Differential Equation Conditional Expectation Embed Markov Chain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag 1984

Authors and Affiliations

  • H. A. P. Blom
    • 1
  1. 1.National Aerospace Laboratory, NLRAmsterdamThe Netherlands

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