Transforming a uniform distribution into a binomial

In summary, if I want to find y such that Y=G(U)~BIN(3,1/2), I need to find G(u) such that FY(y)=G(U). I found this continuous distribution equation that will work for a binomial, but I'm still having trouble finding the discrete equation for the exponential case. Any pointers would be much appreciated.
  • #1
bennyska
112
0

Homework Statement


Let X~UNIF(0,1). Find y = G(u) such that Y = G(U)~BIN(3,1/2)


Homework Equations





The Attempt at a Solution


after a bit of searching/reading, i found how to do this with a continuous distribution (the problem i had was an exponential, so i took the inverse)... however, more searching has not led to any results for the discrete case, which i need for the binomial. any pointers would be definitely appreciated.
 
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  • #2
You might want to take a look at http://en.wikipedia.org/wiki/Inverse_transform_sampling

Basically, given X an arbitrary distribution, and let FX be it's cdf, then we define

[tex]F^{-1}_X(u)=inf\{x~\vert~F(x)=u,~0<u<1\}[/tex].

Then, if U has uniform distribution, then [itex]F_X^{-1}(U)[/itex] has the distribution of X...
 
  • #3
X is continuous whereas Y is not. This implies that G(X) isn't be continuous. Does that help?
 
  • #4
vela said:
X is continuous whereas Y is not. This implies that G(X) isn't be continuous. Does that help?

no... how about one more hint?
 
  • #5
What do you mean, one more hint? I practically gave you the answer in my post!
 
  • #6
Start by figuring out the cdf for Y.
 
  • #7
well, for a binomial, with these parameters, the cdf would be .[tex].5^{3}\sum_{i=0}^{3}\left({3\atop i}\right)[/tex]
 
  • #8
That expression is not the cdf. That's equal to 1. Try again.
 
  • #9
[tex]F_{x}=Pr(X\leq x)=.5^{3}\sum_{i=1}^{x}\left({x\atop i}\right) for
x = 0, 1, 2, 3[\tex]

not sure if this tex will show up, but here's the pdf

(also, basically .53*SUM(from i=0 to x) (x choose i) )
 

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  • #10
Close enough. The lower limit of the summation should be i=0, but other than that it's correct. It's easy enough to just write FX(x) out explicitly.
[tex]F_X(x) = \left\{\begin{array}{cl}
0 & \textrm{if}~x < 0 \\
1/8 & \textrm{if}~~0 \le x \lt 1 \\
1/2 & \textrm{if}~~1 \le x \lt 2 \\
7/8 & \textrm{if}~~2 \le x \lt 3 \\
1 & \textrm{if}~~3 \le x
\end{array}\right.[/tex]
Does this give you an idea of how you might write Y=G(X)?
 
  • #11
That should have been FY(y), not FX(x).
 
  • #12
[tex]G_{X}(y)=\begin{cases}
0 & 0\leq x<1/8\\
1 & 1/8\leq x<1/2\\
2 & 1/2\leq x<7/8\\
3 & 7/8\leq x\end{cases}[/tex]
?
 
  • #13
Cheers! :smile:
 

Related to Transforming a uniform distribution into a binomial

What is a uniform distribution?

A uniform distribution is a probability distribution where all possible outcomes are equally likely to occur. This means that each value within the distribution has the same probability of being selected.

What is a binomial distribution?

A binomial distribution is a discrete probability distribution that describes the number of successes in a fixed number of independent trials. It is characterized by two parameters, the probability of success and the number of trials.

How do you transform a uniform distribution into a binomial distribution?

To transform a uniform distribution into a binomial distribution, you need to specify the number of trials and the probability of success for each trial. Then, you can use a formula called the Bernoulli formula to calculate the probability of obtaining a specific number of successes in those trials.

What is the Bernoulli formula?

The Bernoulli formula is used to calculate the probability of obtaining a specific number of successes in a fixed number of independent trials, given a known probability of success for each trial. It is expressed as P(X=k) = (n choose k) * p^k * (1-p)^(n-k), where n is the number of trials, k is the number of successes, and p is the probability of success.

What are some real-life applications of transforming a uniform distribution into a binomial distribution?

Transforming a uniform distribution into a binomial distribution can be useful in various fields, such as market research, quality control, and medical trials. For example, it can be used to model the success rate of a new product launch, the defect rate of a manufacturing process, or the effectiveness of a new drug treatment.

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