Probability and Random Experiments

In summary: And you are not using everything that has been posted.Basically, you have P(k) = P({k}) = c/(3^k) , for k=1,2,⋯,where c is a constant number. You have a value for the probability ##P(A_1) = P(k = 1)##. So you have a right hand side in terms of c and a left hand side. You have a value for the left hand side. So you should be able to get a value for c. Write out explicitly what you know and see if that helps.I am not sure how to help you any further. I am not sure what you
  • #1
whitejac
169
0

Homework Statement


Problem

Consider a random experiment with a sample space

S={1,2,3,⋯}.

Suppose that we know:

P(k) = P({k}) = c/(3^k) , for k=1,2,⋯,

where c is a constant number.
  1. Find c.
  2. Find P({2,4,6}).
  3. Find P({3,4,5,⋯})
I am primarily interested in part 1, finding C. The rest should follow.

2. Homework Equations

I do not know of any relevant equations other than the three axioms of Probability:

For any even A, P(A) ≥ 0.
Prbability of the sample space S is P(S) = 1.
If a1, a2, a3 are disjoint events, then P(a1∪a2∪a3∪...) = P(a1) + P(a2) + P(a3)...

and the Inclusion Exclusion principle

The Attempt at a Solution


So, I'm a little confused on how to "find" C... I could start plugging in values for k, but then I would just be left with the limit as k → ∞ which would drop C to zero.
P(k= 1) = c/3
P(k=2) = c/9
P(k=3) = c/27
.
.
.
Since C is a constant, it seems a little bit trivial to find it as well as it will dramatically lose its impact in just a few values of k.

Also

My book uses a large Cup (∪) in some of its notations,
P(A) = P(large cup {s_j) = ∑P(sj)
(Beneath both the cup and the sigma are: sj ∈ A)
Does this mean the sum of the unions? I do not believe this helps the current question, but it is located in the vicinity of Random Experiments in my book.
 
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  • #2
whitejac said:

Homework Statement


Problem

Consider a random experiment with a sample space

S={1,2,3,⋯}.

Suppose that we know:

P(k) = P({k}) = c/(3^k) , for k=1,2,⋯,

where c is a constant number.
  1. Find c.
  2. Find P({2,4,6}).
  3. Find P({3,4,5,⋯})
I am primarily interested in part 1, finding C. The rest should follow.

2. Homework Equations

I do not know of any relevant equations other than the three axioms of Probability:

For any even A, P(A) ≥ 0.
Prbability of the sample space S is P(S) = 1.
If a1, a2, a3 are disjoint events, then P(a1∪a2∪a3∪...) = P(a1) + P(a2) + P(a3)...

and the Inclusion Exclusion principle

The Attempt at a Solution


So, I'm a little confused on how to "find" C... I could start plugging in values for k, but then I would just be left with the limit as k → ∞ which would drop C to zero.
P(k= 1) = c/3
P(k=2) = c/9
P(k=3) = c/27
.
.
.
Since C is a constant, it seems a little bit trivial to find it as well as it will dramatically lose its impact in just a few values of k.

Also

My book uses a large Cup (∪) in some of its notations,
P(A) = P(large cup {s_j) = ∑P(sj)
(Beneath both the cup and the sigma are: sj ∈ A)
Does this mean the sum of the unions? I do not believe this helps the current question, but it is located in the vicinity of Random Experiments in my book.

What is preventing you from using the "law" (for disjoint events)
[tex] P(A_1 \cup A_2 \cup A_3 \cup \cdots) = P(A_1) +P(A_2) + P(A_3) + \cdots ? [/tex]
You know what are the probabilities ##P(A_j) = P(k = j), j = 1,2,3, \ldots##, you know these events ##A_j## are disjoint and you should know what the event ##A_1 \cup A_2 \cup A_3 \cup \cdots## on the left represents, and so should know its probability.
 
  • #3
I think I understand that one. What I'm confused about, I guess, is understanding how to correlate that information into a value for C, unless C is the sample space which would be the limit as k increases in value.
(since for finite values, this number would be 1 but in the case of all positive integers then I'm guessing the sum of the unions would be the sample space as all of them would tally to that number?)
 
  • #4
whitejac said:
I think I understand that one. What I'm confused about, I guess, is understanding how to correlate that information into a value for C, unless C is the sample space which would be the limit as k increases in value.
(since for finite values, this number would be 1 but in the case of all positive integers then I'm guessing the sum of the unions would be the sample space as all of them would tally to that number?)

The above statements about C are nonsense; you yourself said in the OP that "c is a constant number". OK, so you don't happen to know yet what is the value of c---in fact, finding it is most of the problem. However, it is not random, does not change, and does not depend on any events; it is just some unknown input parameter. Basically, that is what you told us!
 
  • #5
Okay, I'm sorry. I have zero concept of this particular section. In my opinion the book and professor didn't really cover it.

So, I'm gathering that C is not a random variable. It can't be if it's a constant. That would be any value K.
So this would just be an arbitrary constant? That's what I gather from "basically this is what you told us!" But i don't see how to find it then.
 
  • #6
whitejac said:
Okay, I'm sorry. I have zero concept of this particular section. In my opinion the book and professor didn't really cover it.

So, I'm gathering that C is not a random variable. It can't be if it's a constant. That would be any value K.
So this would just be an arbitrary constant? That's what I gather from "basically this is what you told us!" But i don't see how to find it then.

Your problem description did not use the terminology "arbitrary", and neither did I. I said it was unknown. It is not ANY value K; it is some as-yet-undetermined value. (However, maybe we are not using language in the same way.)

If you use ALL the facts/assumptions underlying probability theory, you ought to see what missing bits of information you need to supply. I am not even sure your difficulties are with the probability aspects; you seem to be having trouble with some standard algebra. You are not using everything you have been taught in the past.

Aside from hinting to you to go back and re-read the fundamental "axioms" of probability, and maybe review basic algebra, I am not allowed to help you more without violating PF rules.
 
  • #7
OP did you ever find C? I don't understand this problem...
 
  • #8
I did. I felt very foolish once I did. It has little do with probability and statistics and much more with ensuring your summation algebra makes sense. Review series, convergence, and use that information to find a way to convert the sum to 1.
 
  • #9
Yes, c must be such that [itex]\frac{c}{3}+ \frac{c}{3^2}+ \frac{c}{3^3}+ \cdot\cdot\cdot= \frac{c}{3}\left(1+ \frac{1}{3}+ \left(\frac{1}{3}\right)^2+ \left(\frac{1}{3}\right)^3+ \cdot\cdot\cdot\right)= 1[/itex]

"Geometric series".
 

FAQ: Probability and Random Experiments

What is probability?

Probability is the measure of the likelihood of an event occurring. It is often expressed as a number between 0 and 1, where 0 represents impossibility and 1 represents certainty.

What is a random experiment?

A random experiment is a process or event that can result in multiple outcomes, and the specific outcome cannot be predicted with certainty. Examples of random experiments include rolling a dice, flipping a coin, or drawing a card from a deck.

What is the difference between theoretical and experimental probability?

Theoretical probability is the calculated probability of an event based on mathematical principles and assumptions. Experimental probability is the observed probability of an event based on actual data collected from repeated trials. Theoretical probability is often used as a basis for comparison to experimental probability.

How is probability calculated?

The probability of an event is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. This can be represented as a fraction, decimal, or percentage.

How is probability used in real life?

Probability is used in many real-life situations, such as in the fields of statistics, economics, and finance. It is also used in everyday decision making, such as determining the likelihood of winning a game or the chances of a certain weather forecast being accurate. Additionally, probability is used in risk assessment and prediction modeling in various industries.

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