http://openworld.existencia.org:80/index.php?action=history&feed=atom&title=Self-Organized_Criticality_in_SD 2026-04-21T22:05:35Z Revision history for this page on the wiki MediaWiki 1.41.0 http://openworld.existencia.org:80/index.php?title=Self-Organized_Criticality_in_SD&diff=130&oldid=prev 2012-04-13T16:19:29Z

<p></p> <p><b>New page</b></p><div>Typically, coupling two models (say, climate and oceans) requires that both be recalibrated, often with parameter values that are not internally reasonable. I&#039;d like to find another option.<br /> <br /> My approach is to consider {X_i} to be an underlying &quot;true&quot; process, which is passed through a parallel collection of noisy channels, which are the models. The goal is uncover an estimate of each X_i. X_i may not be encoded before being passed through the channel.<br /> <br /> If the X_i and Z_i errors are independent, the solution might just be an average or a maximum likelihood. But what if the Z_i&#039;s are correlated? Suppose that the {X_i} come from a Gauss-Markov process with unknown coefficients-- can the coefficients be simultaneously estimated? Suppose that something is known about the power spectrum of X-- how can that be used? The entropy rate is a function of the spectrum, and determines the error bounds on the estimates of X_i, but what is that best estimate?</div>

Jrising