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Projected normal distribution

Projected normal 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 Projected normal distribution rather than just read about it. In short: In directional statistics, the projected normal distribution (also known as offset normal distribution, angular normal distribution or angular Gaussian distribution) is a probability distribution over directions that describes the radial projection of a random variable with n-variate normal distribution over the unit (n-1)-sphere. Definition and properties Given a random variable X ∈ R n {\displaystyle {\boldsymbol…

Key takeaways

  • Projected normal 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 Projected normal distribution to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Projected normal distribution from memory before moving on to harder problems.

Reference excerpt

In directional statistics, the projected normal distribution (also known as offset normal distribution, angular normal distribution or angular Gaussian distribution) is a probability distribution over directions that describes the radial projection of a random variable with n-variate normal distribution over the unit (n-1)-sphere.

Definition and properties Given a random variable X ∈ R n {\displaystyle {\boldsymbol {X}}\in \mathbb {R} ^{n}} that follows a multivariate normal distribution N n ( μ , Σ ) {\displaystyle {\mathcal {N}}_{n}({\boldsymbol {\mu }},\,{\boldsymbol {\Sigma }})} , the projected normal distribution P N n ( μ , Σ ) {\displaystyle {\mathcal {PN}}_{n}({\boldsymbol {\mu }},{\boldsymbol {\Sigma }})} represents the distribution of the random variable Y = X ‖ X ‖ {\displaystyle {\boldsymbol {Y}}={\frac {\boldsymbol {X}}{\lVert {\boldsymbol {X}}\rVert }}} obtained projecting X {\displaystyle {\boldsymbol {X}}} over the unit sphere. In the general case, the projected normal distribution can be asymmetric and multimodal. In case μ {\displaystyle {\boldsymbol {\mu }}} is parallel to an eigenvector of Σ {\displaystyle {\boldsymbol {\Sigma }}} , the distribution is symmetric. The first version of such distribution was introduced in Pukkila and Rao (1988).

Support The support of this distribution is the unit (n-1)-sphere, which can be variously given in terms of a set of ( n − 1 ) {\displaystyle (n-1)} -dimensional angular spherical coordinates:

Θ = [ 0 , π ] n − 2 × [ 0 , 2 π ) ⊂ R n − 1 {\displaystyle {\boldsymbol {\Theta }}=[0,\pi ]^{n-2}\times [0,2\pi )\subset \mathbb {R} ^{n-1}}

or in terms of n {\displaystyle n} -dimensional Cartesian coordinates:

S n − 1 = { z ∈ R n : ‖ z ‖ = 1 } ⊂ R n {\displaystyle \mathbb {S} ^{n-1}=\{{\boldsymbol {z}}\in \mathbb {R} ^{n}:\lVert {\boldsymbol {z}}\rVert =1\}\subset \mathbb {R} ^{n}}

The two are linked via the embedding function, e : Θ → R n {\displaystyle e:{\boldsymbol {\Theta }}\to \mathbb {R} ^{n}} , with range e ( Θ ) = S n − 1 . {\displaystyle e({\boldsymbol {\Theta }})=\mathbb {S} ^{n-1}.} This function is defined by the formula for spherical coordinates at r = 1. {\displaystyle r=1.}

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Projected normal distribution

Start with the simplest possible case. Write down what Projected normal 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 Projected normal 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 Projected normal 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 Projected normal distribution

In research
Projected normal 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 Projected normal 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
Projected normal distribution is common in secondary-school and first-year university syllabi. It links to neighbouring topics Continuous distributions, Directional statistics, Normal distribution, so understanding it makes those chapters shorter.
In everyday life
Look for Projected normal 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 Projected normal distribution in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Projected normal 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 Projected normal distribution out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Projected normal distribution in simple terms?

In directional statistics, the projected normal distribution (also known as offset normal distribution, angular normal distribution or angular Gaussian distribution) is a probability distribution over directions that describes the radial projection of a random variable with n-variate normal distrib…

Why does Projected normal 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 Projected normal 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 Projected normal distribution.

Tags

  • Continuous distributions
  • Directional statistics
  • Normal distribution

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