Hi all.

Today I recieved my copy of

Durbin & Koopman: "Time Series Analysis by State Space Methods"
 Oxford University Press, 2001.

http://www.amazon.com/gp/product/0198523548/sr=8-1/qid=1154184779/ref=sr_1_1/103-3640117-4140620?ie=UTF8

The book is aimed at an audience in statistics and econometrics,
but serves as an intro to general Kalman filters.

I like the general philosophy and structure of the book. As the
authors state on page 1:

"We ... decided ... to devote Chapter 2 of the book to a particularly
simple example of a state space model ... and to develop as many
as possible of the state space techniques for this model. Our hope is
that this will enable readers new to the techniques to gain insights
into the ideas behind state space methodology that will help them
when working through the greater complexities of the general case."

And do the authors succeed! Said chapter 2 starts on page 9, and
withing page 13 -- in four pages -- have they not only sketched
briefly the principles behind the Kalman filter, but also given the
recursive equations for its implementation. The reader can download
the example data from the given website, and try it himself. As a
novice, it is possible to have a working Kalman filter within ten
minutes from opening this book. Granted, some ideas are "dodgy"
and some numbers in the initialization appear as out of thin air,
but what a motivator for further study! After this initial example,
everything else in this book is "merely" a matter of extending the
KF to more dimensions, find efficient implementations and handle
the realistic cases of initialization and estimation of the required
parameters.

I really like this style of presentation, so even if I as of right now
have
read only 16(!) pages, I have no reservations whatsoever against
recommending this book as a primer in Kalman filters.

Rune