Sequential Monte Carlo Methods for Neural Networks

  • N. de Freitas
  • C. Andrieu
  • P. Højen-Sørensen
  • M. Niranjan
  • A. Gee
Chapter
Part of the Statistics for Engineering and Information Science book series (ISS)

Abstract

Many problems, arising in science and engineering, require the estimation of nonlinear, time-varying functions that map a set of input signals to a corresponding set of output signals. Some examples include: finding the relation between an input pressure signal and the movement of a pneumatic control valve; using past observations in a time series to predict future events; and using a group of biomedical signals to carry out diagnoses and prognoses. These problems can be reformulated in terms of a generic one of estimating the parameters of a suitable neural network on-line as the input-output data becomes available.

Keywords

Extended Kalman Filter Class Membership Importance Weight Proposal Distribution Sequential Monte Carlo 
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 Science+Business Media New York 2001

Authors and Affiliations

  • N. de Freitas
  • C. Andrieu
  • P. Højen-Sørensen
  • M. Niranjan
  • A. Gee

There are no affiliations available

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