Dear all. The Normal distribution (or Gaussian) is mostly used in statistics, natural science and engineering. Their importance is linked with the Central Limit Theorem. Is there any ideas how to predict the numbers and parameters of thos Gaussians ? Or any efficient deterministic tool to decompose Gaussian to a finite sum of Gaussian basic functions with parameter estimations ? Thank you in advance.
See the link:
https://www.bing.com/search?q=How+close+can+we+come+to+a+multivariate+Central+Limit+theorem&qs=n&form=QBRE&sp=-1&ghc=1&pq=how+close+can+we+come+to+a+multivariate+central+limit+theorem&sc=1-61&sk=&cvid=5E7DF2492CEC4D20B58B1763E4A7AA82
Best, David Booth
You have to use sample data to estimate the parameters or maximum likelihood methods
Thank you David Eugene Booth and Andrew Paul McKenzie Pegman for responses.
If the data are sequential or otherwise meaningfully parameterized you could try a spectrum analysis and forget about the statistics at first..?
For Central Limit Theorem, the random variables are not necessarily gaussian but they have to be independent and identically distributed (in classical CLT). They can come from any distribution. Moreover, CLT is an approximation. Do you have a prior knowledge that the resultant gaussian variable is composed using gaussian variables? In that case, using the convolutional properties of normal random variables can give you an idea.
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