Analysis of spatial count data using Kalman smoothing

Research output: Book/ReportReportResearch

122 Downloads (Pure)

Abstract

This paper considers spatial count data from an agricultural field experiment. Counts of weed plants in a field have been recorded in a project on precision farming. Interest is in mapping the weed intensity so that the dose of herbicide applied at any location can be adjusted to the amount of weed present at the location. We elaborate on a link between state space models and Markov random fields. The oberservations are modelled as independent Poisson counts conditional on a Gaussian Markov random field. We employ the fact that the model may be written as a state space model which may be analysed by combining approximate Kalman filter techniques with importance sampling.
Original languageEnglish
Place of PublicationAalborg
PublisherDepartment of Mathematical Sciences, Aalborg University
Number of pages9
Publication statusPublished - 2004
SeriesResearch Report Series
NumberR-2004-12
ISSN1399-2503

Fingerprint

Dive into the research topics of 'Analysis of spatial count data using Kalman smoothing'. Together they form a unique fingerprint.

Cite this