Two independent random vectors are almost surely non-orthogonal

In summary, the conversation is about three random vectors of the same length drawn from a continuous random distribution. One of the vectors, Z, is independent of the other two but Y is related to X through a non-linear function. The question is whether it can be claimed that Z^T X ≠ 0 almost surely, or if Y^T X = 0 almost surely. The expert responds by agreeing that the probability of three randomly selected vectors being orthogonal is zero, and provides further clarification on the definitions and assumptions involved in the problem.
  • #1
husseinshatri
1
0
Hi all,

I got stuck with the following problem:

Let X, Y and Z be three random vectors of the same length drawn from a continuous random distribution.

where
Z is independent of X and Y but [itex]Y=f(X)[/itex] with a non-linear function [itex]f[/itex].

Can I claim that:

1. [itex]Z^{T}X\neq 0[/itex] almost surely (i.e., vector X wouldn't almost surely lay on the null space of vector Z),

or

2. [itex]Y^{T}X= 0[/itex] almost surely (i.e., vector X would almost surely lay on the null space of vector Y).

If so, could you give me a hint to the proof or a citation.

Thank you for your help in advance.

Hussein
 
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  • #2
husseinshatri said:
Hi all,

I got stuck with the following problem:

Let X, Y and Z be three random vectors of the same length drawn from a continuous random distribution.

where
Z is independent of X and Y but [itex]Y=f(X)[/itex] with a non-linear function [itex]f[/itex].

Hussein

What do you mean by a random distribution? Are you randomly selecting from a set of distributions? Individual probability distributions are described by functions which are not themselves random. That is, their parameters are specified. Secondly, if three vectors are randomly selected from a population with a known or unknown probability distribution, how do two of these vectors (X and Y) come to be related by a non linear function? I assume you're talking about a population of three component vectors, all of the same length and sharing the same vector space.

I do agree that if you randomly select three vectors from a population where the orientations are randomly distributed on a continuous distribution, the probability that they would be orthogonal is zero. The probability of an exact value on a continuous distribution is zero by the definition of a continuum, in this case: all real numbers on the interval [0,1]. The same would be true for any pair from {X,Y,Z}.

EDIT: If you are talking about three random vectors in a 2-space, then only 2 vectors can be mutually orthogonal, the third being a linear combination of the other two. Was that part of your question?
 
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FAQ: Two independent random vectors are almost surely non-orthogonal

1. What does it mean for two random vectors to be almost surely non-orthogonal?

When two random vectors are almost surely non-orthogonal, it means that there is a very high probability that they are not perpendicular to each other. In other words, the angle between the two vectors is not close to 90 degrees. This is an important concept in probability theory and has applications in various fields such as statistics, physics, and engineering.

2. How is the probability of two random vectors being almost surely non-orthogonal calculated?

The probability of two random vectors being almost surely non-orthogonal is calculated using the concept of almost sure convergence. This involves taking the limit of the probability that the dot product of the two vectors is close to zero (indicating orthogonality) as the number of trials or samples approaches infinity. If the limit is close to zero, then the probability of the vectors being almost surely non-orthogonal is high.

3. Can two random vectors ever be 100% non-orthogonal?

No, it is not possible for two random vectors to be 100% non-orthogonal. This is because the dot product of two vectors must always have a numerical value and cannot be exactly zero. However, the probability of two random vectors being almost surely non-orthogonal can approach 100% as the number of trials or samples increases.

4. What is the significance of two random vectors being almost surely non-orthogonal?

The significance of two random vectors being almost surely non-orthogonal lies in its applications in various fields. In statistics, it is used to test the independence of two random variables. In physics, it is used to determine the stability of a system. In engineering, it is used to analyze the performance of a system. Understanding the probability of two random vectors being almost surely non-orthogonal can provide valuable insights in these areas.

5. How does the concept of almost sure convergence apply to two random vectors being almost surely non-orthogonal?

The concept of almost sure convergence is central to the understanding of two random vectors being almost surely non-orthogonal. It states that if a random event occurs with a probability approaching 1 as the number of trials or samples increases, then the event is said to occur almost surely. In the case of two random vectors being almost surely non-orthogonal, this means that as the number of trials or samples increases, the probability of the vectors being non-orthogonal approaches 1, indicating a high likelihood of non-orthogonality.

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