Modelling noise in Mathematica

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  • #1
madness
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Given a power spectral density Sn(f) (or alternatively the autocorrelation function), is there a way to output random noise in Mathematica? Not sure if anyone here will know this but it's worth a try.
 
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  • #2
Well, you could use Random[NormalDistribution[...]] to generate white noise and then apply an FFT, filter it with your spectral density, and then inverse FFT to get back the original noise. Would that work?
 
  • #3
How do you ?

filter it with your spectral density
 

1. What is noise modelling in Mathematica?

Noise modelling in Mathematica is the process of creating a mathematical representation of random or unpredictable data. It allows for the analysis and simulation of noisy data sets, which can be useful in various scientific fields such as signal processing, statistics, and finance.

2. How does Mathematica handle noise modelling?

Mathematica has built-in functions and tools that allow for the generation and manipulation of random data, as well as various statistical and signal processing functions for noise modelling. It also has a powerful visualization system that can help in analyzing and interpreting the results.

3. Can Mathematica be used for noise modelling in real-world applications?

Yes, Mathematica can be used for noise modelling in a wide range of real-world applications. It has been extensively used in industries such as finance, engineering, and data science for tasks such as forecasting, risk analysis, and signal processing.

4. What types of noise can be modelled in Mathematica?

Mathematica can model various types of noise, including white noise, pink noise, Gaussian noise, and exponential noise. It also has functions for creating custom noise distributions and adding noise to existing data sets.

5. Are there any limitations to noise modelling in Mathematica?

While Mathematica is a powerful tool for noise modelling, it does have some limitations. It may not be suitable for extremely large data sets or complex noise models that require specialized algorithms. Additionally, the accuracy of the results may depend on the quality of the data and the chosen modelling techniques.

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