Probability - transformation of a random variable

In summary, the conversation discusses the process of computing the distribution of Y in an analog to digital conversion, using a quantized function g(x)=[x]+1. The distribution of Y is specified to be the cumulative normal distribution \Phi(y-1) on a finite list of integers with non-zero probability. There is some confusion about the specific formulation of the distribution and whether it is a probability density or mass function. The conversation also mentions the need to compute the probability of Y taking on specific values, such as Y = 1 or Y = -1, based on the probability of the normal random variable X being within certain intervals.
  • #1
dizzle1518
17
0
In an analog to digital conversion and analog waveform is sampled, quantized and coded. A quantized function is a function that assigns to each sample value x a value y from a generally finite set of predetermined values. Consider the quantized defined by g(x)=[x]+1, where [x] denotes the greatest integer less than or equal to x. Suppose that x has a standard normal distribution and pit Y=g(x). Specify the distribution of Y. Ignore values of Y for which the probability is essentially zero.

Going by how the book taught it I would start this problem by computing the inverse of g(x). However this function has no inverse. Any suggestions how to proceed?
 
Physics news on Phys.org
  • #2
What's the probability that Y = 1? For that to happen, X must be in [0,1). You can compute the probability of X being in that interval by using the normal distribution.
 
  • #3
thanks. looks to me like the interval would be (-infinity, y-1). so the distribution of why would be [tex]\Phi(y-1)[/tex]. is this right?
 
  • #4
dizzle1518 said:
thanks. looks to me like the interval would be (-infinity, y-1). so the distribution of why would be [tex]\Phi(y-1)[/tex]. is this right?

Are you taking about the interval to use when computing the cumulative distribution of Y? Yes, that's right if you're using [tex]\Phi [/tex] to denote the cumulative normal.

The wording of the problem indicates that you should specify a finite list of integers on which the density of Y is non-zero.
 
  • #5
i think it just asks for the distribution of Y. How would you know which integers to specify anyway?

I e-mailed my teacher and she replied with the below:

"No, for every y the corresponding interval for x has to be (-infinity,[y]). The endpoint should be an integer."

Does she mean it should be Fy=phi(y)??
 
  • #6
How would you know which integers to specify anyway?

There can't be that many integers with a significant probability. The standard normal has standard deviation = 1. There isn't much chance of getting Y = 12.You didn't say what you asked your teacher, so I don't what her answer meant.
 
  • #7
Stephen Tashi said:
There can't be that many integers with a significant probability. The standard normal has standard deviation = 1. There isn't much chance of getting Y = 12.

got you. then picking up to what numbers you want to define the distribution of Y is up to the person solving the problem?

Stephen Tashi said:
You didn't say what you asked your teacher, so I don't what her answer meant.

i asked her the same thing, i.e. if the interval was (-infinity, y-1) and if the distribution of Y is phi(y-1), and that's what she answered
 
  • #8
dizzle1518 said:
got you. then picking up to what numbers you want to define the distribution of Y is up to the person solving the problem?

It's up to them to do the work. There won't be much disagreement on which integers have non-zero probability if they all use similar tables of the normal distribution.

i asked her the same thing, i.e. if the interval was (-infinity, y-1) and if the distribution of Y is phi(y-1), and that's what she answered

The phrase "if the interval was (-infinity, y-1)" isn't a complete sentence. So I assume she ignored it.

The answer to "if the distribution of Y is phi(y-1)" is no. She answered correctly. The distribution of Y isn't defined on all real numbers, only on integers. Her answer indicates that you only use the calculation on integer values of Y.
 
  • #9
so what would be the distribution of Y then?
 
  • #10
In my opinion, you are trying to give an abstract answer to a problem that wants you to do some numerical work. It wants a list of integers and their probability of occurrence. Are you in doubt about how to compute the numerical probability that Y = 1 or Y = -1 ? I think the problem wants you give the probability density for Y, not the cumulative distribution.

You have the correct idea about using [tex] \phi [/tex]. It's the imprecision in your statement of that idea that your teacher objects to. However, I don't think stating the formula for the cumulative distribution of Y is what the problem requests.
 
  • #11
Stephen Tashi said:
Are you in doubt about how to compute the numerical probability that Y = 1 or Y = -1 ?

a bit yes

Stephen Tashi said:
I think the problem wants you give the probability density for Y, not the cumulative distribution.

would Y have a density or mass function? i thought mass since Y is defined over integers only. the fact that X is continuous and Y discrete is throwing me off somewhat.
 
  • #12
Y =1 exactly when X is between 0 and 1. What's the probability that the normal random variable X with mean 0 and standard deviation 1 is between 0 and 1? (It's about .3413.) The answer to that gives you the probability that Y = 1.
 

What is the definition of a random variable?

A random variable is a numerical quantity that takes on different values based on the outcome of a random event or experiment.

What is the difference between discrete and continuous random variables?

Discrete random variables can only take on a finite or countably infinite number of values, while continuous random variables can take on any value within a given range.

What is the transformation of a random variable?

The transformation of a random variable is the process of changing the original random variable into a new random variable by applying a mathematical function to it.

How is the probability distribution of a transformed random variable related to the original random variable?

The probability distribution of a transformed random variable is related to the original random variable through the transformation function. It can be calculated using the formula P(Y=y) = P(X=x) / |g'(x)|, where X is the original random variable, Y is the transformed random variable, and g(x) is the transformation function.

What is the central limit theorem and how does it relate to transformed random variables?

The central limit theorem states that the sum of a large number of independent and identically distributed random variables will tend towards a normal distribution. This applies to transformed random variables as well, as long as the transformation function is continuous and differentiable.

Similar threads

  • Calculus and Beyond Homework Help
Replies
9
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
439
  • Calculus and Beyond Homework Help
Replies
2
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
30
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
5
Views
470
  • Calculus and Beyond Homework Help
Replies
7
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
919
  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
1K
  • Calculus and Beyond Homework Help
Replies
3
Views
991
Replies
12
Views
733
Back
Top