Gaussian process: Difference between revisions
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<div class="definition"><div class="short_definition">(''Also called'' stationary Gaussian process, stationary Gaussian time series.) A [[stationary time series]] in which the joint [[probability distribution]] of any sequence of values, ''x''(''t''<sub>1</sub>), ''x''(''t''<sub>2</sub>), . . ., ''x''(''t''<sub>''n''</sub>), is a multivariate [[normal distribution]], or a stationary [[random process]] that is completely determined by its [[spectrum]] or [[autocorrelation function]].</div><br/> </div> | <div class="definition"><div class="short_definition">(''Also called'' stationary Gaussian process, stationary Gaussian time series.) A [[stationary time series|stationary time series]] in which the joint [[probability distribution]] of any sequence of values, ''x''(''t''<sub>1</sub>), ''x''(''t''<sub>2</sub>), . . ., ''x''(''t''<sub>''n''</sub>), is a multivariate [[normal distribution]], or a stationary [[random process]] that is completely determined by its [[spectrum]] or [[autocorrelation function]].</div><br/> </div> | ||
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Latest revision as of 16:03, 25 April 2012
Gaussian process
(Also called stationary Gaussian process, stationary Gaussian time series.) A stationary time series in which the joint probability distribution of any sequence of values, x(t1), x(t2), . . ., x(tn), is a multivariate normal distribution, or a stationary random process that is completely determined by its spectrum or autocorrelation function.
