Stochastics: discrete random variables

In summary, in order to find the constants c and d for two independent discrete random variables X1 and X2, we must ensure that the total probability of all possible events in the sample space is equal to 1. This can be achieved by using a geometric series, which converges to 1/2. Therefore, c = 2 and d can be any value as long as it satisfies the condition d = 1/4.
  • #1
sunrah
199
22

Homework Statement


X1 and X2 are two independent discrete random variables with
P(X1 = k) = c3-k
P(X2 = k) = d4-k

for k in natural numbers and where X1, X2 in natural numbers is almost always valid. 0 is not include in N.

Find constants c and d.

Homework Equations




The Attempt at a Solution


Since I'm joining this class late in the semester I don't know where to begin. Any help is appreciated!
 
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  • #2
Hint: what must the total probability of all possible events (in the entire sample space) be?

Another hint: geometric series.
 
  • #3
Curious3141 said:
Hint: what must the total probability of all possible events (in the entire sample space) be?

Another hint: geometric series.

do you mean sum to infinity?

[itex]\Sigma^{\infty}_{K=1} c3^{-k} = 1[/itex]

I see that the series (sn) = c3-k converges.
 
  • #4
sunrah said:
do you mean sum to infinity?

[itex]\Sigma^{\infty}_{K=1} c3^{-k} = 1[/itex]

I see that the series (sn) = c3-k converges.

So what's the sum?
 
  • #5
Curious3141 said:
So what's the sum?

ya, so in this case (sn) = 3-k converges against 1/2. So c = 2

thanks!
 
  • #6
sunrah said:
ya, so in this case (sn) = 3-k converges against 1/2. So c = 2

thanks!

I would've simply said [itex](c)(\frac{1}{2}) = 1 \Rightarrow c = 2[/itex]. But, yes, you've got the idea. :smile:
 

FAQ: Stochastics: discrete random variables

What is a discrete random variable?

A discrete random variable is a type of random variable that can only take on a finite or countably infinite number of values. These values are usually represented by integers, and each value has a specific probability associated with it.

How is a discrete random variable different from a continuous random variable?

A discrete random variable can only take on specific values, while a continuous random variable can take on any value within a certain range. Additionally, the probability distribution of a discrete random variable is represented by a probability mass function, while the probability distribution of a continuous random variable is represented by a probability density function.

What is the importance of stochastics in studying discrete random variables?

Stochastics, or the study of randomness and probability, is important in understanding and analyzing discrete random variables. It allows us to make predictions about the likelihood of certain outcomes, and to make decisions based on the probability of those outcomes occurring.

Can you give an example of a discrete random variable?

One example of a discrete random variable is the outcome of rolling a six-sided die. The possible values are 1, 2, 3, 4, 5, and 6, and each value has a probability of 1/6. Another example is the number of heads when flipping a coin 5 times, with possible values of 0, 1, 2, 3, 4, and 5, each with a probability of 1/32.

How do you calculate the expected value of a discrete random variable?

The expected value of a discrete random variable is calculated by multiplying each possible value by its corresponding probability and then summing all of these values together. For example, if the possible values of a random variable are 1 and 2, with probabilities of 0.4 and 0.6 respectively, the expected value would be (1*0.4) + (2*0.6) = 1.4.

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