Probablity question (confused on the pdf)

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In summary, the conversation revolved around understanding and proving that the given function is a probability density function (pdf). There was a question about the function being bounded by 1, but it was clarified that a pdf does not necessarily have to be bounded by 1. The main focus was on showing that the function integrates to 1 and is bounded within its domain.
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bennyska
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Homework Statement

[itex]
$f(x)=\begin{cases}
7(4)^{-i} & x\in(\frac{1}{2^{i}},\frac{1}{2^{i-1}}],i=1,2,3,...\\
0 & 0\geq x,x>1
\end{cases}$ (please excuse the poor latex)

Homework Equations





The Attempt at a Solution


the problem I'm having is say x=3/4. then according to the pdf, shouldn't P(x)=7*(4^-1)=7/4>1. i mean, shouldn't it be bounded by 1? when i integrate this out, i get 1, like i should, so I'm not sure what mistake I'm making.
 
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  • #2
what is the actual question? Maybe I'm missing it, but it seems like you just posted an equation without any description of what you are trying to do
 
  • #3
Why do you expect that a pdf must be bounded by 1? Consider a random variable that is uniformly distributed over the interval [0, 1/2]. Is that pdf bounded by 1?
 
  • #4
well, the actual problem is show that this function is a pdf. so i need to show that it integrates to 1, which it does, but i believe i also need to show that it's bounded by 0 and 1 for all x in the domain, which is the problem I'm having a hard time with.
 
  • #5
oh, got it. duh. thank you.
 

FAQ: Probablity question (confused on the pdf)

What is a probability density function (PDF)?

A probability density function (PDF) is a mathematical function that describes the likelihood of a random variable taking on a certain value. It is used to represent the probability distribution of a continuous random variable.

How is a PDF different from a probability mass function (PMF)?

A probability mass function (PMF) is used to represent the probability distribution of a discrete random variable, while a probability density function (PDF) is used for continuous random variables. A PMF assigns a probability to each possible outcome, while a PDF assigns a probability to a range of values.

What is the relationship between a PDF and a cumulative distribution function (CDF)?

The cumulative distribution function (CDF) is the integral of the probability density function (PDF). It represents the probability that a random variable will have a value less than or equal to a given value. In other words, the CDF is the area under the PDF curve.

How do you interpret the shape of a PDF?

The shape of a PDF can provide information about the likelihood of different values for a random variable. A PDF with a higher peak or more pronounced center indicates a higher likelihood of values around that point. Flat or uniform PDFs indicate an equal likelihood of all values. Skewed PDFs can indicate a bias towards certain values.

How do you calculate probabilities using a PDF?

To calculate the probability of a random variable falling within a certain range, you can use the integral of the PDF over that range. This will give you the area under the curve for that range, which represents the probability. Alternatively, you can use the CDF to calculate the probability of a random variable being less than or equal to a given value.

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