Stochastic resonance uses noise to amplify weak impulses while HMM approach models the observation of a system as a probabilistic function of the system hidden states.
Furthermore, the observation of a system does not affect its later behavior or, if observation somehow should influence this behavior, it is always possible to incorporate the effect into the prediction of the system's later state.
We address the question of using large-scale transcriptomic observation of a system perturbation to analyze a regulatory network which contained several types of interactions - transcriptional and post-translational.
If the observation region ω is replaced by γ ⊂ Γ, the Carleman estimate with ( m − 1 ) observations for a system of m ( m ≥ 2 ) coupled parabolic equations remains an open question.
Observations of a hydrologic system response are needed to accurately model system behaviour.
The behaviours of the species in the target system can be obtained from observations of a biochemical system from the wet laboratory and corresponding computational model in dry laboratory.
Data assimilation methodologies are designed to incorporate noisy observations of a physical system into an underlying model in order to infer the properties of the state of the system.
Given observations of a network system's behaviours or measurements of its functional dynamics, what can we conclude of the details of physical connectivity of the underlying structure?
Example 2. Given an attack scenario α = 〈s0, a1, s1〉, an aggregated network evidence which was generated starting from the evidence collected from the different network observers, a set describing the observation function obs of a system security solution and the evidence O = 〈o1, o2〉 it generated when the attack scenario α was executed, and a set of initial system states denoted by St.
