A question about uniform distributions

In summary, the pdf for a uniform distribution on [0,x] is 1/x. However, if the support is [0,x), there is no difference in the pdf as the probability of measuring a single point is zero in both cases. The only slight difference is that for [0,x] it is possible to measure exactly x, while for [0,x) it is impossible. But overall, the pdfs are the same.
  • #1
Artusartos
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0
If we have a uniform distribution on [0,x]...then the pdf is 1/x right? But what if we have [0,x)? Do we still have the same pdf?

Thanks in advance.
 
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  • #2
Since x is usually used as a variable, it would be better to write your pdf as being uniform on [0,a], and so the pdf is ##\rho_X(x) = 1/a##. There is effectively no difference if you take the support of the pdf to be [0,a), as you have only removed a single point from the distribution, and the probability of measuring that point is zero in both cases. I suppose there is a slight technical difference in that in practice if the support is [0,a] it is possible that the result of a measurement would be exactly a, whereas for [0,a) it is impossible, but the pdf's are still the same.

(Even though the probability of drawing any specific number is zero, when you draw from a continuous probability distribution you always get some number, so even if something technically has zero probability of occurring, you can still draw it if it is in the support of the pdf.)
 

1. What is a uniform distribution?

A uniform distribution is a statistical distribution where all possible outcomes have equal probability of occurring. This means that each value in the distribution has the same likelihood of being observed.

2. How is a uniform distribution different from other distributions?

A uniform distribution is different from other distributions in that it has a constant probability density function, whereas other distributions may have varying probabilities for different outcomes.

3. What are some examples of events that follow a uniform distribution?

Some examples of events that follow a uniform distribution include rolling a fair die, selecting a card from a deck, and picking a random number between two given values.

4. How is a uniform distribution useful in scientific research?

A uniform distribution can be useful in scientific research as it allows for a more objective and unbiased approach to analyzing data. It can also be used to model random processes and make predictions based on probability.

5. Can a uniform distribution have a different range of values?

Yes, a uniform distribution can have a different range of values. The probability for each value within the range will still be equal, but the range itself can vary depending on the specific situation or experiment being studied.

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