Fourier Series

One of the earliest problems in geometric probability, it is considered a precursor to the Monte Carlo method. It involves dropping a needle of length \(l\) onto a floor with parallel lines a distance \(t\) apart. The probability that the needle will cross a line is \(P = \frac{2l}{\pi t}\) (for \(l \le t\)). This provides a physical experiment to estimate \(\pi\).

Parseval's theorem relates the total energy of a signal (the integral of its square over one period) to the sum of the squared energies of its Fourier series components. For a function \(s(x)\) with period \(P\), the theorem states: \(\frac{1}{P} \int_P |s(x)|^2 , dx = \sum_{n=-\infty}^{\infty} |c_n|^2\), where \(c_n\) are the complex Fourier coefficients.

Bayesian inference is a statistical method where Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is a central tenet of Bayesian statistics. The core idea is expressed as: posterior probability is proportional to the product of the prior probability and the likelihood, \(p(\theta|D) \propto p(D|\theta)p(\theta)\), where \(\theta\) is the parameter and D is the data.

Theorema Egregium (Latin for "Remarkable Theorem") states that the Gaussian curvature of a surface is an intrinsic property. This means it depends only on how distances are measured on the surface itself, not on how the surface is embedded in three-dimensional space. A flat sheet of paper can be rolled into a cylinder but not a sphere without stretching.

For a Fourier series to converge to the function's value, the function must satisfy the Dirichlet conditions over one period. These are: (1) the function must be absolutely integrable, (2) it must have a finite number of extrema (maxima and minima), and (3) it must have a finite number of finite discontinuities.

De Morgan's laws are a pair of transformation rules in Boolean algebra that are fundamental to digital circuit design. The first law states that the negation of a conjunction is the disjunction of the negations: \(\neg(P \land Q) \iff (\neg P) \lor (\neg Q)\). The second states that the negation of a disjunction is the conjunction of the negations: \(\neg(P \lor Q) \iff (\neg P) \land (\neg Q)\).

Bayes' theorem describes the probability of an event based on prior knowledge of conditions that might be related to the event. It is a fundamental concept in probability theory and statistics. Mathematically, it is stated as \(P(A|B) = \frac{P(B|A)P(A)}{P(B)}\), where A and B are events and \(P(B) \neq 0\). It relates the conditional and marginal probabilities of two random events.

A second-order linear elliptic partial differential equation that describes systems in a steady-state or equilibrium condition. It is written as \(nabla^2 u = 0\) or \(Delta u = 0\), where \(nabla^2\) (or \(Delta\)) is the Laplace operator. Solutions, called harmonic functions, are the smoothest possible functions and represent potentials in fields like electrostatics, gravitation, and fluid flow.

A standard approach for approximating solutions to overdetermined systems by finding model parameters that minimize the sum of the squared differences between observed and predicted values. This sum is known as the sum of squared residuals (SSR). The goal is to find the parameters \(\hat{\beta}\) that minimize the function \(S(\beta) = \sum_{i=1}^{n} (y_i - x_i^T \beta)^2\).

A fundamental second-order linear parabolic partial differential equation describing heat distribution or other diffusion processes. Its canonical form is \(\frac{partial u}{partial t} = \alpha \nabla^2 u\), where \(u(\vec{x},t)\) is temperature, \(t\) is time, and \(\alpha\) is thermal diffusivity. Solutions model how an initial temperature distribution evolves, smoothing out irregularities over time and approaching a steady state.

The coefficients for the Fourier series of a function \(s(x)\) with period \(P\) are calculated using integral formulas. The DC component is \(a_0 = \frac{2}{P} \int_{P} s(x) , dx\). The cosine coefficients are \(a_n = \frac{2}{P} int_{P} s(x) \cos\left(\frac{2pi n x}{P}\right) , dx\), and the sine coefficients are \(b_n = \frac{2}{P} \int_{P} s(x) \sin\left(\frac{2\pi n x}{P}\right) , dx\) for \(n ge 1\).

A fundamental solution of a linear partial differential operator \(L\) is a solution to the equation \(Lu = delta(x)\), where \(delta(x)\) is the Dirac delta function. It represents the response of the system to a point source or impulse. Once known, the solution to the inhomogeneous equation \(Lu = f(x)\) can be found by convolution: \(u(x) = (G * f)(x)\), where \(G\) is the fundamental solution.

The Gauss-Bonnet theorem connects the geometry of a compact two-dimensional surface to its topology. It states that the integral of the Gaussian curvature \(K\) over the entire surface \(M\) is equal to \(2\pi\) times the Euler characteristic \(\chi(M)\) of the surface. The formula is \(\int_M K \, dA = 2\pi \chi(M)\).

Accuracy refers to the closeness of a measured value to a standard or known true value. Precision refers to the closeness of two or more measurements to each other. A measurement system can be precise but not accurate, accurate but not precise, neither, or both. These two concepts are independent of each other in measurement theory.