Abstract. Gaussian process prior systems generally consist of noisy measurements of samples of the putatively Gaussian process of interest, where the samples serve to constrain the...
With the growing complexity in computer systems, it has been a real challenge to detect and diagnose problems in today’s large-scale distributed systems. Usually, the correlatio...
The goal of input-output modeling is to apply a test input to a system, analyze the results, and learn something useful from the causeeffect pair. Any automated modeling tool that...
We study the dynamics of a simple bistable system driven by multiplicative correlated noise. Such system mimics the dynamics of classical attractor neural networks with an addition...
: System identification is the art and science of building mathematical models of dynamic systems from observed input-output data. It can be seen as the interface between the real ...