Chapter 6 — A Unifying View: Hilbert Spaces

Companion material for Chapter 6. Shows how MMSE estimation, the Wiener filter, and linear least squares are all instances of orthogonal projection in a Hilbert space.


§ 6.1 Inner Product Spaces

Inner Product as Similarity

The correlation coefficient \rho is the cosine of the angle between two zero-mean random variables in L^2(\Omega). This demo lets you adjust \rho and see the geometry change in real time.

Hilbert Spaces in This Course

🎥 3Blue1Brown — Abstract vector spaces: extending linear algebra to function spaces and general vector space axioms. Directly motivates the Hilbert space framing of this chapter.

Probability Hilbert Space L^2(\Omega)

The autocorrelation function is the inner product in L^2(\Omega). This demo makes the connection between the inner product geometry and the computed ACF values tangible.


§ 6.2 Signal Representations

Basis Expansion

🎥 3Blue1Brown — Fourier series as a basis expansion: sine and cosine functions form an orthonormal basis in L^2[0,T]. The projection formula c_n = \langle f, e_n \rangle is the same formula used in this chapter.


§ 6.3 Projections and the Gramian

§ 6.4 Unifying View

🎥 3Blue1Brown — Dot products as projections, and the duality between vectors and linear functionals. The geometric core of the unifying view.