Please help me with Gaussian Distribution and central limit theorem in matlab

In summary, the conversation discusses the use of Gaussian distribution and the central limit theorem in MATLAB. The speaker is seeking help in generating a random variable with an approximate Gaussian distribution and producing a graph to visualize the distribution. They also mention the distribution of the instantaneous power for a signal with a Gaussian amplitude distribution. A suggestion is given to use the randn function instead of rand, and an example of adjusting the parameters is provided. The speaker asks for the code developed so far to better understand where they are stuck.
  • #1
blgna2
4
0
please help me with Gaussian Distribution and central limit theorem in matlab!

:cry:I am trying to generate a random variable with a approximate gaussian distribution using the rand function and central limit theorem, got stuck when trying it. Please help me. Also want to know how to produce a graph showing the distribution of the values the
program generates and the graph of a Gaussian distribution.
If a signal has a Gaussian amplitude distribution, what is the distribution of the instantaneous power? Graph the power distribution generated using the Central Limit Theorem.

Help me!:cry::confused:
 
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  • #2


Dear please provide the code you have developed till now so I can see what changes you should make or what you should add...
 
  • #3


How can we know where you are stuck?

By simply guessing, maybe you are having problems with rand, that's a uniform distribution, try randn which will give you Gaussian with mean 0 and sd 1. You can element-wise multiply to adjust the parameters, eg 5 + 2.*randn(6,6) will give you a 6x6 matrix of Gaussian random numbers, each with mean 5 and standard deviation 2.
 

1. What is Gaussian Distribution?

Gaussian Distribution, also known as normal distribution, is a statistical distribution that is commonly used to model real-world data. It is characterized by a bell-shaped curve and is symmetrical around the mean. Many natural phenomena, such as height and weight measurements, follow a Gaussian Distribution.

2. How is Gaussian Distribution related to Central Limit Theorem?

The Central Limit Theorem states that when independent random variables are added together, their sum tends to follow a normal distribution, regardless of the distribution of the individual variables. This means that even if the data does not follow a Gaussian Distribution, the sum of many random variables will approximate a Gaussian Distribution.

3. What is the importance of Gaussian Distribution and Central Limit Theorem in MATLAB?

MATLAB is a powerful statistical software that allows for the analysis and manipulation of data. Gaussian Distribution and Central Limit Theorem are fundamental concepts in statistics and are frequently used in data analysis and modeling. Understanding these concepts in MATLAB can help improve the accuracy and efficiency of data analysis.

4. How can I generate random numbers from a Gaussian Distribution in MATLAB?

To generate random numbers from a Gaussian Distribution in MATLAB, you can use the randn function. This function generates random numbers from a standard normal distribution, which is equivalent to a Gaussian Distribution with a mean of 0 and a standard deviation of 1. You can then use the mean and std functions to adjust the mean and standard deviation of the generated numbers.

5. Can I visualize a Gaussian Distribution in MATLAB?

Yes, you can visualize a Gaussian Distribution in MATLAB by using the normpdf function to generate a probability density curve and the normcdf function to generate a cumulative distribution curve. These functions take in the mean and standard deviation of the distribution as inputs. You can also use the histogram function to plot a histogram of data that follows a Gaussian Distribution.

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