Central Limit Theorem Proof: Expanding the Exponential

In summary, the conversation is about a question regarding a proof of the Central-Limit theorem and the relationship between cumulants and moments. The person is asking for someone to explicitly show how the equalities in the next lines of the proof stem from expanding out the exponential and comparing powers of k. The expert suggests looking at the definitions of cumulants and moments to better understand the relationship.
  • #1
PineApple2
49
0
Hello. This is the most closely matching forum I found for this, so I hope my question fits here. I was looking at the following proof of the Central-Limit theorem:
http://physics.ucsc.edu/~peter/250/deriv_climit.pdf
and after Eq. (10) it says: "Expanding out the exponential in the last expression and comparing
powers of k one finds that the fi rst few cumulants are..."
but I don't see how the equalities in the next lines stem from it.
Could someone please explicitly show that?

Thanks.
 
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  • #2
PineApple2 said:
Hello. This is the most closely matching forum I found for this, so I hope my question fits here. I was looking at the following proof of the Central-Limit theorem:
http://physics.ucsc.edu/~peter/250/deriv_climit.pdf
and after Eq. (10) it says: "Expanding out the exponential in the last expression and comparing
powers of k one finds that the fi rst few cumulants are..."
but I don't see how the equalities in the next lines stem from it.
Could someone please explicitly show that?

Thanks.

Hey PineApple2.

I think the best way for you would be to look at how the moments and cumulants are defined with respect to each other:

http://en.wikipedia.org/wiki/Cumulant
http://en.wikipedia.org/wiki/Moment_(mathematics)

Specifically with regard to your question:

http://en.wikipedia.org/wiki/Cumulant#Cumulants_and_moments
 

What is the Central Limit Theorem?

The Central Limit Theorem is a fundamental concept in statistics that states that the distribution of sample means of a population will tend to follow a normal distribution, regardless of the underlying distribution of the population, as the sample size increases.

Why is the Central Limit Theorem important?

The Central Limit Theorem is important because it allows us to make statistical inferences about a population using a smaller sample. This is especially useful when studying large populations where it may be impractical or impossible to collect data from every individual.

What is the proof of the Central Limit Theorem?

The proof of the Central Limit Theorem involves using mathematical concepts such as the Law of Large Numbers and the Central Limit Theorem to show that as the sample size increases, the distribution of sample means will approach a normal distribution.

How does the Central Limit Theorem relate to the Expanding the Exponential method?

The Expanding the Exponential method is a technique used to prove the Central Limit Theorem. It involves expanding the exponential function to a Taylor series and using properties of the exponential distribution to show that the distribution of sample means approaches a normal distribution.

What are the implications of the Central Limit Theorem in practical applications?

The Central Limit Theorem has many practical applications, including in market research, quality control, and hypothesis testing. It allows us to make accurate inferences about a population using a smaller sample, which can save time and resources. It also provides a framework for understanding and analyzing data in a wide range of fields.

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