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Holtsmark distribution

Holtsmark distribution is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Holtsmark distribution rather than just read about it. In short: The (one-dimensional) Holtsmark distribution is a continuous probability distribution. The Holtsmark distribution is a special case of a stable distribution with the index of stability or shape parameter α {\displaystyle \alpha } equal to 3/2 and the skewness parameter β {\displaystyle \beta } of zero.

Holtsmark distribution — main illustration
Holtsmark distribution — illustration

Key takeaways

  • Holtsmark distribution belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Holtsmark distribution to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Holtsmark distribution from memory before moving on to harder problems.

Reference excerpt

The (one-dimensional) Holtsmark distribution is a continuous probability distribution. The Holtsmark distribution is a special case of a stable distribution with the index of stability or shape parameter α {\displaystyle \alpha } equal to 3/2 and the skewness parameter β {\displaystyle \beta } of zero. Since β {\displaystyle \beta } equals zero, the distribution is symmetric, and thus an example of a symmetric alpha-stable distribution. The Holtsmark distribution is one of the few examples of a stable distribution for which a closed form expression of the probability density function is known. However, its probability density function is not expressible in terms of elementary functions; rather, the probability density function is expressed in terms of hypergeometric functions. The Holtsmark distribution has applications in plasma physics and astrophysics. In 1919, Norwegian physicist Johan Peter Holtsmark proposed the distribution as a model for the fluctuating fields in plasma due to the motion of charged particles. It is also applicable to other types of Coulomb forces, in particular to modeling of gravitating bodies, and thus is important in astrophysics.

Characteristic function The characteristic function of a symmetric stable distribution is:

φ ( t ; μ , c ) = exp ⁡ [ i t μ − | c t | α ] , {\displaystyle \varphi (t;\mu ,c)=\exp \left[~it\mu \!-\!\left|ct\right|^{\alpha }\right],}

where α {\displaystyle \alpha } is the shape parameter, or index of stability, μ {\displaystyle \mu } is the location parameter, and c is the scale parameter. Since the Holtsmark distribution has α = 3 / 2 , {\displaystyle \alpha =3/2,} its characteristic function is:

φ ( t ; μ , c ) = exp ⁡ [ i t μ − | c t | 3 / 2 ] . {\displaystyle \varphi (t;\mu ,c)=\exp \left[~it\mu \!-\!\left|ct\right|^{3/2}\right].}

Since the Holtsmark distribution is a stable distribution with α > 1, μ {\displaystyle \mu } represents the mean of the distribution. Since β = 0, μ {\displaystyle \mu } also represents the median and mode of the distribution. And since α < 2, the variance of the Holtsmark distribution is infinite. All higher moments of the distribution are also infinite. Like other stable distributions (other than the normal distribution), since the variance is infinite the dispersion in the distribution is reflected by the scale parameter, c. An alternate approach to describing the dispersion of the distribution is through fractional moments.

Probability density function In general, the probability density function, f(x), of a continuous probability distribution can be derived from its characteristic function by:

f ( x ) = 1 2 π ∫ − ∞ ∞ φ ( t ) e − i x t d t . {\displaystyle f(x)={\frac {1}{2\pi }}\int _{-\infty }^{\infty }\varphi (t)e^{-ixt}\,dt.}

Most stable distributions do not have a known closed form expression for their probability density functions. Only the normal, Cauchy and Lévy distributions have known closed form expressions in terms of elementary functions. The Holtsmark distribution is one of two symmetric stable distributions to have a known closed form expression in terms of hypergeometric functions. When μ {\displaystyle \mu } is equal to 0 and the scale parameter is equal to 1, the Holtsmark distribution has the probability density function:

… excerpt ends here. Continue reading the full article.

Illustrations

Holtsmark distribution illustration
Holtsmark distribution illustration

Worked examples

Example 1 — a first encounter with Holtsmark distribution

Start with the simplest possible case. Write down what Holtsmark distribution claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Holtsmark distribution before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Holtsmark distribution ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Holtsmark distribution

In research
Holtsmark distribution appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Holtsmark distribution in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Holtsmark distribution is common in secondary-school and first-year university syllabi. It links to neighbouring topics Continuous distributions, Location-scale family probability distributions, Power laws, so understanding it makes those chapters shorter.
In everyday life
Look for Holtsmark distribution outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Holtsmark distribution in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Holtsmark distribution means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Holtsmark distribution out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Holtsmark distribution in simple terms?

The (one-dimensional) Holtsmark distribution is a continuous probability distribution. The Holtsmark distribution is a special case of a stable distribution with the index of stability or shape parameter α {\displaystyle \alpha } equal to 3/2 and the skewness parameter β {\displaystyle \beta } of z…

Why does Holtsmark distribution matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Holtsmark distribution?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Holtsmark distribution.

Tags

  • Continuous distributions
  • Location-scale family probability distributions
  • Power laws
  • Probability distributions with non-finite variance
  • Stable distributions

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