Discrete Convolutions, How to?

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In summary, a discrete convolution is a mathematical operation used to combine two discrete functions and create a third function. It is commonly used in various scientific fields, such as engineering, statistics, and computer science, to analyze signals and images. The calculation involves multiplying two functions and summing the products over a specific range of values. The purpose of discrete convolution is to extract useful information from the relationship between two functions, and it has many applications in audio and video processing, image processing, computer vision, pattern recognition, and data analysis. However, it has limitations, including the assumption of discrete functions, linearity and time-invariance, and the need for a large amount of computation. It may also not be suitable for analyzing non-stationary
  • #1
bookworm121
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How would you go about convolving two discrete distributions that look something like this:

Number: 0, 1, 2
Probability: 0.1, 0.3, 0.6

Number: 0, 1, 2, 3, 4
Probability: 0.1, 0.3, 0.2, 0.1, 0.3
 
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  • #2
bookworm121 said:
How would you go about convolving two discrete distributions

The usual kind of convolution computes the probability distribution of the sum of two independent random variables. is that the kind you want? (There is another type of convolution called "circular convolution".)
 
  • #3
[itex]\sum_{i= 0}^n f(i) g(n- i)[/itex]

Since the second, g, is defined for i= 0, 1, 2, 3, and 4, and the first, f, only for 0, 1, and 2, extend f by setting f(3)= f(4)= 0. f(0)g(4)+ f(1)g(3)+ f(2)g(2)+ f(3)g(1)+ f(4)g(0).
 

Related to Discrete Convolutions, How to?

What is a discrete convolution?

A discrete convolution is a mathematical operation that combines two discrete functions to create a third function. It is commonly used to analyze signals and images in various scientific fields, including engineering, statistics, and computer science.

How is discrete convolution calculated?

Discrete convolution is calculated by multiplying two discrete functions and then summing the products over a specific range of values. This process is repeated for each value of the range to create a new discrete function.

What is the purpose of discrete convolution?

The purpose of discrete convolution is to analyze the relationship between two functions and to extract useful information from them. It is commonly used in signal processing to filter noisy signals, in image processing to blur or sharpen images, and in statistics to smooth data.

What are some applications of discrete convolution?

Discrete convolution has many applications in various scientific fields. It is commonly used in audio and video processing, image processing, computer vision, pattern recognition, and data analysis. It is also used in engineering for system modeling, simulation, and control.

What are the limitations of discrete convolution?

Discrete convolution has some limitations, including the assumption that the functions being convolved are discrete and that the convolution process is linear and time-invariant. It also requires a large amount of computation for complex functions and may introduce artifacts in the resulting function. Additionally, it may not be suitable for analyzing non-stationary signals or signals with high-frequency components.

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