Probability (probability mass function,pmf)

In summary, the conversation discusses a probability mass function and its relation to the total probability being 1. There is a mention of a sum and its value being 1. The conversation also touches on an exponential probability distribution and its connection to calculus.
  • #1
naspek
181
0
If X has the probability mass function f(x) = k / x! (x=0,1,2,...),
what is the value of k?
 
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  • #2
I think you mean "probability density function".

The total probability must be 1.
[itex]\sum_{x=0}^\infty k/x!= k\sum_{x=0}^\infty 1/x!= 1[/itex]

Do you know what that sum is?
 
  • #3
HallsofIvy said:
I think you mean "probability density function".

The total probability must be 1.
[itex]\sum_{x=0}^\infty k/x!= k\sum_{x=0}^\infty 1/x!= 1[/itex]

Do you know what that sum is?
i don't know.. is it infinity?
 
  • #4
For discrete distributions these are usually called mass functions - density is reserved for continuous distributions.

naspek, HallsofIvy is asking whether you can identify what the sum

[tex]
\sum_{x=0}^\infty \frac 1 {x!}
[/tex]

equals? This problem should remind you of a commonly used distribution.
 
  • #5
You probably have not taken calculus yet, but if you had you would have learned that
[tex]e^x= \sum_{n=0}^\infty \frac{x^n}{n!}[/tex].

Do you know what an "exponential probability distribution" is?
 
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  • #6
HallsofIvy said:
You probably have not taken calculus yet, but if you had you would have learned that
[tex]e^x= \sum_{n=0}&\infty \frac{x^n}{n!}[/tex].

Do you know what an "exponential probability distribution" is?

ok.. now i understand already... thanks guys! =)
 

FAQ: Probability (probability mass function,pmf)

1. What is a probability mass function (pmf)?

A probability mass function (pmf) is a mathematical function that describes the probability of a random variable taking on a specific value. It maps each possible value of the random variable to its probability of occurring.

2. How is a pmf different from a probability density function (pdf)?

A pmf is used for discrete random variables, while a pdf is used for continuous random variables. A pmf gives the probability of a specific value occurring, while a pdf gives the probability of a range of values occurring.

3. What are the properties of a pmf?

A pmf must satisfy two properties: 1) The probabilities must be non-negative, and 2) the sum of all probabilities must equal 1. This ensures that all possible outcomes are accounted for and that the probabilities are accurate.

4. How is a pmf used in probability calculations?

A pmf is used to calculate the probability of a specific outcome or set of outcomes occurring. It can also be used to calculate the mean, variance, and other statistical measures of a random variable.

5. Can a pmf be used for continuous random variables?

No, a pmf is only applicable for discrete random variables. For continuous random variables, a probability density function (pdf) must be used.

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