Finding Probability Generating Function for H0

In summary, the conversation discusses finding the probability generating function for a sequence of independent identically distributed random variables, where the probabilities are given for the first variable. It is possible to find the values of X2, X3,... Xn by using the given probabilities of Y1.
  • #1
hsong9
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1

Homework Statement


Let Y1,Y2,... be independent identically distributed random variables with
P(Y1=1) = P(Y1=-1) = 1/2 and set X0 = 1, Xn=X0 + Y1 +...+ Yn for n >= 1. Define
H0 = inf { n >= 0 : Xn = 0}.
Find the probability generating function Φ(s) = E(sH0).


Homework Equations





The Attempt at a Solution


Actually, this is grad. level course, but
I do not have much some background about probability.
I don't know whether I can get the values of X2, X3,... Xn by using Y1 =1 = Y1 = -1...
Is it possible?
 
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  • #2


Hello, thank you for your question. Yes, it is possible to find the values of X2, X3,... Xn by using the given information about Y1. Since Y1 is an independent identically distributed random variable, it means that each Yn has the same distribution with the same probabilities. Therefore, you can use the given probabilities of Y1 to calculate the probabilities of Y2, Y3,... Yn and then use those probabilities to calculate the values of X2, X3,... Xn. Once you have the values of X2, X3,... Xn, you can use them to calculate the probability generating function Φ(s) = E(sH0). I hope this helps.
 

1. What is a probability generating function?

A probability generating function (PGF) is a mathematical tool used in probability theory to describe the probability distribution of a discrete random variable. It is a generating function that encodes the probabilities of all possible values of a random variable into a single function.

2. What is the purpose of finding the probability generating function for H0?

Finding the probability generating function for H0 allows us to determine the probability distribution of a discrete random variable under the null hypothesis. This is useful in hypothesis testing, where we compare the observed data to the expected distribution under the null hypothesis to assess the significance of our results.

3. How do you find the probability generating function for H0?

The probability generating function for H0 can be found by expressing the null hypothesis as a polynomial function and using the properties of PGFs to simplify and solve for the coefficients. Alternatively, it can also be found by taking the inverse Laplace transform of the moment generating function of the null hypothesis.

4. What is the relationship between the probability generating function and the moment generating function?

The probability generating function and the moment generating function are both generating functions that describe the probability distribution of a random variable. However, the PGF is only defined for discrete random variables, while the MGF is defined for both discrete and continuous random variables. The PGF is also a special case of the MGF, where the argument is restricted to be a non-negative integer.

5. Can the probability generating function for H0 be used to find the probability of a specific outcome?

Yes, the probability generating function for H0 can be used to find the probability of a specific outcome by evaluating the function at the desired value. However, it is more commonly used to find the probabilities of all possible outcomes, rather than a single specific outcome.

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