Separate noise from signal if I know the noise

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SUMMARY

The discussion focuses on techniques to separate noise from data derived from a negative binomial distribution with additive Gaussian noise. The user has experimented with basic mean noise subtraction and a Wiener filter, both of which have shown promising results. The consensus suggests that the Wiener filter is more suitable than the Kalman filter for this specific scenario. Further exploration of advanced noise reduction techniques is encouraged.

PREREQUISITES
  • Understanding of negative binomial distribution
  • Familiarity with Gaussian distribution and its properties
  • Knowledge of Wiener filter and its applications
  • Basic concepts of noise reduction techniques
NEXT STEPS
  • Research advanced noise reduction techniques beyond Wiener filtering
  • Explore the implementation of Kalman filters in similar contexts
  • Study the statistical properties of negative binomial distributions
  • Investigate the impact of different noise models on data analysis
USEFUL FOR

Data scientists, statisticians, and researchers working with statistical distributions and noise reduction techniques in data analysis.

eoghan
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Hi all!
I have this problem: I have some data that come from a negative binomial distribution with unknown parameters and some additive noise with gaussian distribution of known parameters.
What is the best way to remove the noise from the distribution of the data? I tried bot a very basic mean noise subtraction and a Wiener filter and they seems to work quite well, but I wonder if there are better techniques.

Thank you!
 
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Wiener is likely more appropriate than Kalman in this case.
 

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