]> 2026-07-08T04:26:33+00:00 Linear System Identifiability from Distributional and Time Series Data 0 en We consider identifiability of linear systems driven by white noise using a combination of distributional and time series measurements. Specifically, we assume that the system has no control inputs available and can only be observed at stationarity. The user is able to measure the full stationary state distribution as well as observe time correlations for small subsets of the state. We formulate theoretical conditions on identifiability of parameters from distributional information alone. We then give a sufficient condition and an effective necessary condition for identifiability using a combination of distributional and time series measurements. We illustrate the ideas with some simple examples as well as a biologically inspired example of a transcription and degradation process. Anandh Swaminathan and Richard M. Murray 2015h 2016 American Control Conference (ACC) sm16-acc Conference Paper 2016-05-16T21:57:40Z 2457525.4150463 Linear System Identifiability from Distributional and Time Series Data Title 102 en Title