What Does Gaussian Smearing Mean in Histograms?

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SUMMARY

Gaussian smearing in histograms refers to the process of applying a Gaussian function to smooth the data represented in the histogram. This technique involves integrating a Gaussian-approximated delta function to create an approximate step function. To implement Gaussian smearing in code, one must multiply each bin of the histogram by the Gaussian function to achieve the desired smoothing effect.

PREREQUISITES
  • Understanding of histogram data representation
  • Familiarity with Gaussian functions and their properties
  • Basic programming skills for implementing mathematical functions
  • Knowledge of numerical integration techniques
NEXT STEPS
  • Research Gaussian functions and their applications in data smoothing
  • Learn about histogram data structures and manipulation techniques
  • Explore numerical integration methods for approximating functions
  • Implement Gaussian smearing in a programming language such as Python or C++
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Data analysts, statisticians, and software developers involved in data visualization and analysis who need to apply smoothing techniques to histogram data.

eoghan
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Hi!
I was asked to smear an histogram with a gaussian smearing, but I don't understand what does it mean. Can you please explain me what is a gaussian smearing?
Thank you
 
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eoghan said:
Hi!
I was asked to smear an histogram with a gaussian smearing, but I don't understand what does it mean. Can you please explain me what is a gaussian smearing?
Thank you

Gaussian smearing is an approximate step function obtained by integration of a
Gaussian-approximated delta function...
 
Uhm... ok... but how can I implement it in a code? I have to multiply each bin of the histogram with this step function?
 
Last edited:
eoghan said:
Hi!
I was asked to smear an histogram with a gaussian smearing, but I don't understand what does it mean. Can you please explain me what is a gaussian smearing?
Thank you

Did you find an answer to your question? I have the same question as well
 

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