• Eckhard PlatenEmail author
  • Nicola Bruti-Liberati
Part of the Stochastic Modelling and Applied Probability book series (SMAP, volume 64)


A very powerful approach that allows us to extract, in an adaptive manner, information from observed date is that of filtering. The aim of this chapter is to introduce filtering of information about hidden variables that evolve over time. These variables may follow continuous time hidden Markov chains or may satisfy certain hidden SDEs. Their observation is considered to be perturbed by the noise of Wiener or other processes. Approximate discrete-time filters driven by observation processes will be constructed for different purposes.


Wiener Process Time Step Size Contingent Claim Observation Process Strong Order 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.School of Finance and Economics, Department of Mathematical SciencesUniversity of Technology, SydneyBroadwayAustralia

Personalised recommendations