Probability function with specification for different range ?

In summary, the speaker explains that a probability function can be defined according to a specific range, and for each range, a different function may be used. This can be applied to calculate probabilities by using integration. The speaker also mentions that for expectation, variance, etc, the same integration process can be used by splitting the integral into appropriate intervals. They also mention that when the upper limit of the range approaches a discrete value, it can be treated as a separate function. As long as the probability density function is valid, this approach can be used.
  • #1
RufusDawes
156
0
I understand a probability function can be defined according to range ?

So for example,

0>x f(x) = 0

for 0>x>100 f(x) = 1/100

to work out probability it is integration of that function.

So how does it work if for some other range there is a DIFFERENT functions ?

Is it that there will be multiple equations for probability depending on x ?
 
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  • #2
RufusDawes said:
I understand a probability function can be defined according to range ?

So for example,

0>x f(x) = 0

for 0>x>100 f(x) = 1/100

to work out probability it is integration of that function.

So how does it work if for some other range there is a DIFFERENT functions ?

Is it that there will be multiple equations for probability depending on x ?

Hey RufusDawes.

You do the same thing, but break the integral up into pieces for each appropriate interval.

For expectation, variance etc: same thing: split up the integral according to the intervals and do the integration to get mean, variance, etc.
 
  • #3
chiro said:
Hey RufusDawes.

You do the same thing, but break the integral up into pieces for each appropriate interval.

For expectation, variance etc: same thing: split up the integral according to the intervals and do the integration to get mean, variance, etc.

So what do I do when the upper limit of the range approaches a discrete value (a whole number) on both ranges.

If there are 2 functions one with a limit 0>x>1 and 1>=x>2 and the other is for x>=2 the integrals of the whole thing should = 1 ?

does that mean that I can use the value of 1 as the upper bit for the first integral as it is so small it won't affect the area ?
 
  • #4
RufusDawes said:
So what do I do when the upper limit of the range approaches a discrete value (a whole number) on both ranges.

If there are 2 functions one with a limit 0>x>1 and 1>=x>2 and the other is for x>=2 the integrals of the whole thing should = 1 ?

does that mean that I can use the value of 1 as the upper bit for the first integral as it is so small it won't affect the area ?

You treat function 1 in range 1 and function 2 in range 2. As long the PDF is a valid probability density function, then it's ok to do this. Are the functions for 0 > x > 1 and 1 >= x > 2 analytic? In other words can you for each range describe a continuous analytic function?
 
  • #5



Yes, it is possible to have multiple equations for the probability function depending on the range of x. This is because the probability function can be tailored to fit the specific scenario or data being analyzed. For example, if the range of x is from 0 to 100, the probability function may be defined as f(x) = 1/100. However, if the range of x is from 100 to 200, the probability function may be defined as f(x) = 1/200. In this case, there would be two different equations for the probability function depending on the range of x. The specific equation used would depend on the specific range being analyzed. It is important to carefully define the range and corresponding equations for the probability function in order to accurately calculate probabilities.
 

1. What is a probability function?

A probability function is a mathematical function that assigns a probability to each possible outcome of a random variable. It is used to model the likelihood of different events occurring.

2. How is a probability function specified?

A probability function is typically specified by providing a range of possible outcomes and their corresponding probabilities. This can be done through a table, graph, or mathematical formula.

3. What is the difference between discrete and continuous probability functions?

A discrete probability function is used for discrete random variables, which can only take on a finite or countably infinite number of values. A continuous probability function is used for continuous random variables, which can take on any value within a given range.

4. Can a probability function be used for non-numerical data?

Yes, a probability function can be used for non-numerical data by assigning numerical values to each possible outcome. For example, a coin flip can be represented as a binary random variable with the outcomes "heads" and "tails" assigned the values 0 and 1, respectively.

5. How is the accuracy of a probability function determined?

The accuracy of a probability function is determined by how well it aligns with actual observed data. It can be evaluated through various statistical measures, such as mean squared error or the coefficient of determination.

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