How Do We Handle Sub-Events in Function Normalization?

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In summary, the conversation discusses the concept of normalization of functions, specifically in relation to Gaussian functions in quantum mechanics. The purpose of normalization is to ensure that the total probability of all possible outcomes is equal to 1. There is also mention of the possibility of a larger space and the limitations of our understanding. The conversation ends with a request for additional ideas on the topic.
  • #1
manesh
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hello
I need more ideas about the normalization of functions..like Guassian..
I think this is to make the possibility of all events =1.(like in Q. mechanics we normalize wavefunctions). So the area under the curve is constant..
if anybody has more ideas please reply
manesh
 
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  • #2
manesh said:
hello

Hello manesh, and welcome to Physics Forums.

I need more ideas about the normalization of functions

Well...

I think this is to make the possibility of all events =1.

Yes, that's it right there. That's the only reason we normalize wavefunctions: so that the total probability of all possible outcomes is 1. There's nothing deeper than that to it.

Or are you asking about how to do it?
 
  • #3
Tom Mattson said:
Yes, that's it right there. That's the only reason we normalize wavefunctions: so that the total probability of all possible outcomes is 1. There's nothing deeper than that to it.
Yes, but what happens when we recognize that some event we normalize to 1 is itself just a sub-event in a larger space? All we do know with absolute certainty is that the universe as a whole exists. The probability of the universe is 1. Everything else has less that a probability of 1.
 
  • #4
I know how to do it..just asked if somebody has more ideas... :biggrin:
 
  • #5
Mike2 said:
Yes, but what happens when we recognize that some event we normalize to 1 is itself just a sub-event in a larger space?

Nothing changes, because we already recognize that. We simply do not have total information on the complete state of the universe, so when we do QM we have to consider an idealized system in isolation from the rest of the universe. Since the approach agrees well with experiment, I have no problem with it.
 

FAQ: How Do We Handle Sub-Events in Function Normalization?

What is the purpose of normalizing functions?

The purpose of normalizing functions is to transform data into a standardized range, typically between 0 and 1, so that it can be easily compared and analyzed. This helps to eliminate the influence of varying scales and units in the data.

What are the common methods for normalizing functions?

Some common methods for normalizing functions include min-max scaling, z-score standardization, and decimal scaling. Each method has its own advantages and disadvantages, so the choice of method depends on the specific data and analysis goals.

How does normalizing functions affect the shape of data?

Normalizing functions do not change the shape of the data, but rather compress or stretch it to fit within a specific range. This can make it easier to compare and visualize data, but it does not alter the underlying distribution of the data.

Can normalizing functions be applied to any type of data?

Normalizing functions can be applied to most types of data, including numerical, categorical, and textual data. However, the method of normalization may vary depending on the type of data and the analysis goals.

What are some potential drawbacks of normalizing functions?

One potential drawback of normalizing functions is that they can distort or hide important features in the data. Additionally, some methods of normalization may be sensitive to outliers or extreme values. It is important to carefully consider the data and analysis goals before applying normalization techniques.

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