# What is the convolution of two Gaussians?

## What is the convolution of two Gaussians?

It is well known that the product and the convolution of two Gaussian probability density functions (PDFs) are also Gaussian. The product of two Gaussian PDFs is proportional to a Gaussian PDF with a mean that is half the coefficient of x in Eq.

**What is a convolution of two functions?**

In mathematics (in particular, functional analysis), convolution is a mathematical operation on two functions (f and g) that produces a third function ( ) that expresses how the shape of one is modified by the other. The term convolution refers to both the result function and to the process of computing it.

**Is the sum of two Gaussians an Gaussian?**

A Sum of Gaussian Random Variables is a Gaussian Random Variable. That the sum of two independent Gaussian random variables is Gaussian follows immediately from the fact that Gaussians are closed under multiplication (or convolution).

### What is the product of two Gaussians?

The product of two Gaussian PDFs is proportional to a Gaussian PDF with a mean that is half the coefficient of x in Eq. 5 and a standard deviation that is the square root of half of the denominator i.e. as, due to the presence of the scaling factor, it will not have the correct normalisation.

**What is the convolution of two vectors?**

The convolution of two vectors, u and v , represents the area of overlap under the points as v slides across u . Algebraically, convolution is the same operation as multiplying polynomials whose coefficients are the elements of u and v .

**What happens when you add two gaussians?**

This means that the sum of two independent normally distributed random variables is normal, with its mean being the sum of the two means, and its variance being the sum of the two variances (i.e., the square of the standard deviation is the sum of the squares of the standard deviations).

#### How do you prove that two normal distributions are independent?

A very important property of jointly normal random variables, and which will be the starting point for our development, is that zero correlation implies independence. If two random variables X and Y are jointly normal and are uncorrelated, then they are independent.

**Is a product of gaussians a Gaussian?**

It is well known that the product and the convolution of Gaussian probability density functions (PDFs) are also Gaussian functions. The product of two Gaussian PDFs is proportional to a Gaussian PDF with a mean that is half the coefficient of x in Eq.

**How to prove convolution of two Gaussian functions is again a Gaussian?**

In class I mentioned the result that the convolution of two gaussian functions is again a gaussian. This is usually proved by: showing that the Fourier transform \\(\\hat{f}\\)of a gaussian \\(f\\)is a gaussian;

## What is the difference between Gaussian convolution and Fourier transform?

the Fourier transform (FT) of a Gaussian is also a Gaussian The convolution in frequency domain (FT domain) transforms into a simple product then taking the FT of 2 Gaussians individually, then making the product you get a (scaled) Gaussian and finally taking the inverse FT you get the Gaussian

**What is the a Gaussian function?**

A gaussian is a function of the form \\(f(x)=Ae^{-(x-\\mu)^2/2\\sigma^2}\\)for some constant \\(A\\)– when \\(A\\)is chosen to make the total integral of \\(f\\)equal to \\(1\\), you obtain the probability distribution function for a normally distributed random variable of mean \\(\\mu\\)and variance \\(\\sigma^2\\).

**What is the sum of independent Gaussian random variables?**

The sum of independent Gaussian random variables is Gaussian. The marginal of a joint Gaussian distribution is Gaussian. The conditional of a joint Gaussian distribution is Gaussian.