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Covariance

ValkanPavlov Mathematics 27 August 2026

Covariance is a statistical measure that indicates the direction of the linear relationship between two random variables. It tells us whether, when one variable increases, the other tends to increase or decrease. Think of it as a way to capture whether two quantities move in unison or in opposite directions.

For example, if we observe the height and weight of a group of people, we expect a positive covariance – taller people generally weigh more. Conversely, if we look at a car's speed and stopping distance, the covariance is positive (higher speed → longer stopping distance). If we compare outdoor temperature and heating bills, the covariance is negative – the colder it gets, the higher the bills.

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Vector

ValkanPavlov Mathematics 24 August 2026

A vector is a mathematical object that has both magnitude (length) and direction. You can think of it as an arrow pointing from one point to another. Vectors are one of the most fundamental tools in mathematics, physics, engineering, computer science, and data science — anywhere where quantities have both size and orientation.

Imagine you are standing in a city and ask for directions to a park. The answer "3 kilometres" tells you the distance, but not where to go — that's a scalar (just a number). The answer "3 kilometres north-east" tells you both how far and in which direction — that's a vector.

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Long division

ValkanPavlov Mathematics 24 August 2026

The long division algorithm is a systematic procedure for dividing multi-digit numbers. It breaks the division problem into a sequence of simpler steps: divide, multiply, subtract, bring down, and repeat. This method works for any integers, regardless of their size, and can be extended to decimals and polynomials.

Imagine you have 847 candies to share equally among 7 friends. Long division gives you a foolproof, repeatable process that guarantees the correct result. It is the arithmetic equivalent of following a recipe — each step is clear and leads naturally to the next.

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Hilbert Transform

ValkanPavlov Mathematics 20 August 2026
Hilbert Transform – Dictionary Entry

The Hilbert transform is a linear operator that takes a real-valued signal \( x(t) \) and produces another real-valued signal \( \hat{x}(t) \), often called its quadrature counterpart. In the time domain, it is defined as the convolution of \( x(t) \) with the kernel \( 1/(\pi t) \):

\( \mathcal{H}\{x(t)\} = \hat{x}(t) = \dfrac{1}{\pi} \cdot \text{p.v.} \int_{-\infty}^{\infty} \dfrac{x(\tau)}{t - \tau} \, d\tau \)

where p.v. denotes the Cauchy principal value, which handles the singularity at \( \tau = t \). In practice, the Hilbert transform is most easily understood in the frequency domain.

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Еxponential

ValkanPavlov Mathematics 18 August 2026

The exponential function is one of the most powerful and universal concepts in mathematics. In its simplest form, it describes a quantity that grows (or decays) at a rate proportional to its current value. The standard form is f(x) = a·eᵏˣ, where e ≈ 2.718 is Euler's number, but any base works – 2ˣ, 10ˣ, or even (1 + r)ˣ for compound interest. The defining feature is that the function's derivative equals itself (for eˣ) or is proportional to itself – growth feeds on growth.

Think of it this way – you have a savings account with 100% annual interest, compounded continuously. You start with 1 USD. After one year, you have about 2.718 USD (that is e¹). After two years, you have e² ≈ 7.39 USD. The growth is not linear; it accelerates because the interest you earn also starts earning interest. This is the essence of exponential growth – every new increment adds to the base that produces the next increment.

Read more: Еxponential
  1. Logarithm
  2. Normalisation
  3. Bode plot
  4. The Bessel function

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