How to quantize the ''mathematical'' fluctuation field in statistics?

In summary, the conversation discusses the effort to quantize fields, including mathematical fields and continuous fields in physics. The functional integral formalism is considered as a tool for calculating correlation functions, but may not provide information about quantization. The possibility of posing commutators and anticommutators for any fields in physics is also discussed, and it is suggested that the functional formalism can deduce the quantum of fields for any field that is not integrable. However, a non-linear and non-quantizable field is considered to be a white elephant.
  • #1
ndung200790
519
0
Please teach me this:
The general effort is to quantize the fields of elementary particles and gravitons.But I wonder about ''mathematical'' fields such as the fluctuation fields in statistical physics.I think there may be many ''continuous'' fields in physics.Could the functional integral formalism
say any things about ''the quantum'' of field?Because this formalism is only a powerful tool to canculate the correlation function,but say nothing(it seem to me) about the quantization(about ''quantum'' of fields).
Thank you very much in advance
 
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  • #2
Then could we always pose the commutators and anticommutators for any fields in physics or not?(with the theories are renormalizable)
 
  • #3
It seem to me that because the canonical and functional quantization have the exactly same result.Then the functional formalism also deduces the ''discontiuous'' quantum of fields.So any fields can represent by functional integral formalism are ''quantizable''.Is that correct?
 
  • #4
ndung200790 said:
It seem to me that because the canonical and functional quantization have the exactly same result.Then the functional formalism also deduces the ''discontiuous'' quantum of fields.So any fields can represent by functional integral formalism are ''quantizable''.Is that correct?

Yes.

Any field that is not integrable is discounted by the renormalization conditions.

A field that is non linear and non quantizable is a white elephant.
 

Related to How to quantize the ''mathematical'' fluctuation field in statistics?

1. What is quantization in statistics?

Quantization in statistics is the process of converting a continuous variable, such as a fluctuation field, into a discrete variable. This involves dividing the range of values into smaller intervals and assigning a single value to each interval. This allows for easier analysis and comparison of data.

2. Why is quantization important in statistics?

Quantization is important in statistics because it allows for the simplification and organization of data. It also helps in reducing the amount of data needed for analysis, making it more efficient and manageable.

3. How is quantization different from discretization?

Quantization and discretization are often used interchangeably, but they have subtle differences. Quantization involves dividing a continuous variable into a finite number of values, while discretization involves dividing a continuous variable into a finite number of intervals or categories. In other words, quantization assigns a single value to each interval, while discretization allows for multiple values within an interval.

4. What are the common methods used for quantization?

The most commonly used methods for quantization include rounding, truncation, and quantization error. Rounding involves rounding off the values to the nearest whole number, while truncation involves dropping the decimal values. Quantization error is the difference between the original continuous variable and the quantized version.

5. How does quantization affect the accuracy of statistical analysis?

Quantization can affect the accuracy of statistical analysis if not done properly. It can lead to loss of information and introduce errors in the data. This is why it is important to carefully choose the method of quantization and consider the potential impact on the accuracy of the results.

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