What is the Advantage of Using a Gaussian Distribution of Noise in a System?

In summary: There are many good books on random numbers and probability theory, as well as chaos and stochastic resonance.
  • #1
m~ray
31
0
what is the advantage of using a gaussian distribution of noise (white/colored) in a system over any other distribution ??
 
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  • #2
Do you mean other than the fact that this is what random noise actually looks like..?
 
  • #3
Are you talking 'simulations'?
 
  • #4
okay so u mean to say all random number distributions naturally follow the gaussian distribution?? say we need to insert 10^10 numbers between 1-100, where all the 100 places are equally likely to be filled. so eben in this case u mean to say on an avg there will be more number of numbers in 51 and 50?? if yes, then why should it be like that, as all numbers are equally likely to be filled..
 
  • #5
No. Say you are picking numbers 1-100 completely at random, and you do this 10^10 times (This is the same as what you said, just phrased slightly differently). Now, the mean number of times you picked a given number will be 10^8 (10^10/100), but there will be some variation around this 10^8 mean and this variation will be described by a gaussian.
 
  • #6
m~ray said:
okay so u mean to say all random number distributions naturally follow the gaussian distribution?? say we need to insert 10^10 numbers between 1-100, where all the 100 places are equally likely to be filled. so eben in this case u mean to say on an avg there will be more number of numbers in 51 and 50?? if yes, then why should it be like that, as all numbers are equally likely to be filled..
You are misinterpreting what the Gaussian distribution means, I think. Each number is equally likely: no more 50s than 99s. But it is more likely that adding any random two together will give an answer nearer to 100 than to 200 or 2. So it's the average and not the individual number that counts.
 
  • #7
thanks a lot for the explanations. yes, the variation about the mean 10^8 describing a gaussian curve makes total sense now..

by the way, i am doing a project in stochastic resonance, and i want to learn more about stochastic processes, random numbers, probability theory, chaos and other related areas in the coming summer. Can u name me a few good books or other resources on such subjects?
 

FAQ: What is the Advantage of Using a Gaussian Distribution of Noise in a System?

What is a Gaussian noise distribution?

A Gaussian noise distribution, also known as a normal distribution, is a probability distribution commonly used in statistics and data analysis. It is characterized by a symmetric bell-shaped curve, with the majority of data points clustered around the mean and a small percentage of data points in the tails.

How is Gaussian noise distributed?

Gaussian noise is distributed according to the Gaussian or normal distribution, which follows a specific mathematical formula. This distribution is often used to model natural phenomena, such as the heights of individuals in a population, due to its prevalence in nature.

What are the properties of a Gaussian noise distribution?

The properties of a Gaussian noise distribution include a symmetrical shape, with the mean, median, and mode all being equal. It also has a defined standard deviation, which measures the spread of the data around the mean. Additionally, the distribution is continuous, meaning that it can take on any value within a certain range.

What is the significance of Gaussian noise distribution?

Gaussian noise distribution is significant in statistics and data analysis because it is a commonly occurring natural phenomenon and can be used to model many real-world processes. It is also widely used in statistical tests and machine learning algorithms to make predictions and draw conclusions from data.

How is Gaussian noise distribution used in data analysis?

Gaussian noise distribution is used in data analysis to understand and model the underlying patterns and trends in a dataset. It is often used to identify outliers and anomalies in the data and can also help in making predictions and drawing conclusions from the data. Additionally, many statistical tests and machine learning algorithms rely on the assumption of a normal distribution in order to be valid.

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