Kernel methods have emerged as a powerful tool in adaptive filtering and system identification, enabling the processing and modelling of complex, nonlinear relationships in dynamic systems. By mapping ...

For an arbitrary Hilbert space 𝓔, the Segal–Bargmann space 𝓗(𝓔) is the reproducing kernel Hilbert space associated with the kernel K(x, y) = exp(〈x, y〉) for x, y in 𝓔. If φ : 𝓔₁ → 𝓔₂ is a ...

Hilbert spaces provide a fundamental mathematical framework for analysing infinite-dimensional vector spaces endowed with an inner product. In the context of stochastic processes, these spaces serve ...