পদ্ধতির তুলনা করুন

	স্থানিক কালম্যান ফিল্টার	কালম্যান ফিল্টার
ক্ষেত্র	বেইসীয়	বেইসীয়
পরিবার	Bayesian methods	Bayesian methods
উদ্ভবের বছর	1960 (base); spatial extensions 1990s–2000s	1960
প্রবর্তক	R. E. Kalman (base filter, 1960); extended to spatial settings by Cressie, Wikle and colleagues	Rudolf E. Kalman
ধরন	Bayesian state-space model	recursive Bayesian filter
মৌলিক উৎস	Cressie, N. & Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. Wiley. ISBN: 978-0-471-69274-4	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
অপর নাম	spatial state-space filter, spatio-temporal Kalman filter, SKF, spatial dynamic linear model	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
সম্পর্কিত	6	5
সারসংক্ষেপ	The spatial Kalman filter applies classical Kalman filtering to spatio-temporal state-space models, treating a spatially distributed latent field as the hidden state that evolves over time. At each time step, the filter recursively predicts the spatial field forward and then updates the prediction with new spatial observations, producing optimal linear estimates of the field and its uncertainty across all locations.	The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time.