We present a regularized non-casual consistent estimator for this problem and analyze its sample complexity over two regimes: (a) where the interval $T$ consists of $n$ i. i. d.
To the best of our knowledge, this is the largest scale power modeling study of this kind, in both the scope of diverse datacenter planning and real-time management use cases, as well as the variety of hardware configurations and workload types used for modeling and validation.
The majority of prior work in consistent topology estimation relies on dynamical systems excited by temporally uncorrelated processes.
Transport of intracellular cargo is often mediated by teams of molecular motors that function in a chaotic environment and varying conditions.
Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines.
In this article, we present a method to learn the interaction topology of a network of agents undergoing linear consensus updates in a non invasive manner.
For grids that include cycles of length three, we provide sufficient conditions that ensure existence of algorithms for exact reconstruction.
In this article we present a method to reconstruct the interconnectedness of dynamically related stochastic processes, where the interactions are bi-directional and the underlying topology is a tree.