Tapis kalman ruang waktu=Space time Kalman filter

Many physical or biological processes involve variability over both space and time. A large datas,et and the modelling of space, time, and spatio-temporal interaction cause traditional space time methods are limited. This paper presents an approach to space time prediction that achieves dimension re...

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Main Author:	Perpustakaan UGM, i-lib
Format:	Article NonPeerReviewed
Published:	[Yogyakarta] : Universitas Gadjah Mada 2005
Subjects:	Jurnal i-lib UGM
Online Access:	https://repository.ugm.ac.id/18021/ http://i-lib.ugm.ac.id/jurnal/download.php?dataId=796
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Institution:	Universitas Gadjah Mada

id	id-ugm-repo.18021
record_format	dspace
spelling	id-ugm-repo.180212014-06-18T00:27:35Z https://repository.ugm.ac.id/18021/ Tapis kalman ruang waktu=Space time Kalman filter Perpustakaan UGM, i-lib Jurnal i-lib UGM Many physical or biological processes involve variability over both space and time. A large datas,et and the modelling of space, time, and spatio-temporal interaction cause traditional space time methods are limited. This paper presents an approach to space time prediction that achieves dimension reduction and uses a statistical model that is temporally dynamic and spatially descriptive, called space time Kalman filter. The model also allows a non dinamic spatial component. Key Words : prediction, filter, optimal prediction, Bayesian inference, orthonormal basis. [Yogyakarta] : Universitas Gadjah Mada 2005 Article NonPeerReviewed Perpustakaan UGM, i-lib (2005) Tapis kalman ruang waktu=Space time Kalman filter. Jurnal i-lib UGM. http://i-lib.ugm.ac.id/jurnal/download.php?dataId=796
institution	Universitas Gadjah Mada
building	UGM Library
country	Indonesia
collection	Repository Civitas UGM
topic	Jurnal i-lib UGM
spellingShingle	Jurnal i-lib UGM Perpustakaan UGM, i-lib Tapis kalman ruang waktu=Space time Kalman filter
description	Many physical or biological processes involve variability over both space and time. A large datas,et and the modelling of space, time, and spatio-temporal interaction cause traditional space time methods are limited. This paper presents an approach to space time prediction that achieves dimension reduction and uses a statistical model that is temporally dynamic and spatially descriptive, called space time Kalman filter. The model also allows a non dinamic spatial component. Key Words : prediction, filter, optimal prediction, Bayesian inference, orthonormal basis.
format	Article NonPeerReviewed
author	Perpustakaan UGM, i-lib
author_facet	Perpustakaan UGM, i-lib
author_sort	Perpustakaan UGM, i-lib
title	Tapis kalman ruang waktu=Space time Kalman filter
title_short	Tapis kalman ruang waktu=Space time Kalman filter
title_full	Tapis kalman ruang waktu=Space time Kalman filter
title_fullStr	Tapis kalman ruang waktu=Space time Kalman filter
title_full_unstemmed	Tapis kalman ruang waktu=Space time Kalman filter
title_sort	tapis kalman ruang waktu=space time kalman filter
publisher	[Yogyakarta] : Universitas Gadjah Mada
publishDate	2005
url	https://repository.ugm.ac.id/18021/ http://i-lib.ugm.ac.id/jurnal/download.php?dataId=796
_version_	1681217190750060544

Tapis kalman ruang waktu=Space time Kalman filter

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