Proving if a function is a valid probability distribution

In summary, the conversation discusses how to prove if a given function, P, is a probability distribution. The main focus is on proving the function is bounded by 0 and 1, and the conversation also mentions how to prove that a function is not a valid probability distribution by showing that at least one of the conditions is not true.
  • #1
kioria
54
0
Hi,

Given the function:

[tex]P_{k} = \frac{20}{5^{k}}[/tex] for [tex]k \geq 2[/tex]

How would you prove that P is a probability distribution? I would think that you prove that P is bounded by 0 and 1 (i.e., [tex]0 \leq \Sigma P_{k} \geq 1[/tex])

And I guess the leading question is how you would prove that a function is not a probability distribution?
 
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  • #2
You also need that
[tex]\sum_{k=2}^\infty\frac{20}{5^k}=1[/tex]
 
  • #3
You would prove that a function is NOT a valid probability distribution by showing that at least one of those conditions is not true. That is, that
1) Pk < 0 for some k or
2) Pk > 1 for some k or
3) [tex]\sum_{k=2}^\infty\frac{20}{5^k}\ne 1[/tex]
 
  • #4
Cheers
 

1. What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of an event or set of events occurring. It assigns a probability to each possible outcome of an experiment or random variable.

2. How do you determine if a function is a valid probability distribution?

A function is considered a valid probability distribution if it meets two criteria: 1) the sum of all probabilities is equal to 1, and 2) all probabilities are non-negative. This can be checked by evaluating the function for all possible outcomes and ensuring that the sum is equal to 1 and all values are greater than or equal to 0.

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

A discrete probability distribution is one in which the possible outcomes are countable and have a finite or countably infinite number of possible values. Examples include coin flips or rolling a die. A continuous probability distribution is one in which the possible outcomes are uncountable and have an infinite number of possible values. Examples include measuring the height of a person or the time it takes to complete a task.

4. Can a function be both a valid probability distribution and a valid cumulative distribution function?

Yes, a function can be both a valid probability distribution and a valid cumulative distribution function (CDF). The CDF is the cumulative sum of probabilities up to a certain point, and it must start at 0 and end at 1. As long as the original function meets the criteria for a valid probability distribution, it can also be a valid CDF.

5. How are probability distributions used in real-world applications?

Probability distributions are used in a variety of fields, including statistics, finance, engineering, and science. They can be used to model and analyze data, make predictions, and inform decision-making. Some common applications include risk assessment, quality control, and market analysis.

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