Skip to main content

A General Recursive Filter

  • Chapter
Hidden Markov Models

Part of the book series: Stochastic Modelling and Applied Probability ((SMAP,volume 29))

  • 1700 Accesses

In this chapter more general models are considered. We use again the same reference measure methods. Both nonlinear with nonadditive noise and linear dynamics are considered. In Section 5.7 the results are extended to a parameter estimation problem. In this case the same noise enters the signal and observations. In Section 5.8, an abstract formulation is given in terms of transition densities. Finally, in Section 5.9 we discuss a correlated noise case, where the noise in the observations appears in the state dynamics as well.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

(1995). A General Recursive Filter. In: Hidden Markov Models. Stochastic Modelling and Applied Probability, vol 29. Springer, New York, NY. https://doi.org/10.1007/978-0-387-84854-9_5

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-84854-9_5

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94364-0

  • Online ISBN: 978-0-387-84854-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics