And in that case the Gaussian distribution, the Gaussian probability density looks

In the last video, we talked about the Gaussian distribution.

Gaussian distribution that is centered at zero since Mu is zero.

Gaussian distribution with some parameter Mu and some parameter sigma squared.

In Kalman filters the distribution is given by what's called a Gaussian.

Let's look at some examples of what the Gaussian distribution looks like.

In this video, I'd like to talk about the Gaussian distribution, which is also called the normal distribution.

So, if you're given a data set like this, you know, it looks like maybe, if I estimate what Gaussian distribution the data came from, maybe that might be roughly the Gaussian distribution it came from, with Mu being the center of the distribution and sigma the standard deviation controlling the width of this Gaussian distribution.

Then we have a Gaussian distribution that is centered around zero, because that's Mu.

Gaussian distribution looks like, as well as how to estimate the parameters, mu and sigma squared, of the Gaussian distribution, and if you're given a training set, that is if you're given a set of data that you suspect comes from a Gaussian distribution with unknown parameters using sigma squared.

Gaussian

Gaussian

If we pluck the Gaussian distribution or Gaussian probability density, it will look like the bell shaped curve, which you may have seen before.

In the next video, I'll talk a bit about the Gaussian distribution and review properties of the Gaussian probability distribution, and in videos after that, we will apply it to develop an anomaly detection algorithm.

Gaussian sharpness

Gaussian Blur

Spectral stars that are like those that make elliptical convolved with say Gaussian distribution of Doppler shifts.

In case you're already intimately familiar with the Gaussian distribution, it is probably okay to skip this video.

And in the video after this, we'll start applying the Gaussian distribution to developing an anomaly detection algorithm.

Gaussian Noise Reducer

That's called a Gaussian. We discuss the Gaussian in depth.

And the parameter estimation problem is, let's say I suspect that these examples came from a Gaussian distribution so

Mu and of sigma squared, that is, if it corresponds to a Gaussian distribution, that then looks like this.

And finally, one last example would be, if we now changed the Mu parameters as well, then instead of being centered at zero, we now we have a Gaussian distribution that is centered at three, because this shifts over the entire Gaussian distribution.

For large N, say N large N 30, we know that the distribution of µ you might observe is Gaussian.

Here's our Gaussian again.

Distribution

Distribution

Distribution

So, this script's end stands for normal, since Gaussian and normal distribution, they mean the same phase of synonymous and a

I have another Gaussian question for you this is called a heavytail Gaussian.

That's this little tilde as distributed as and then to denote the Gaussian Distribution, sometimes you're going to write script n, parentheses

Distribution Lists

Distribution List

T distribution.

T distribution.

T distribution.

But if you're not sure or if it's been a while since you've worked with a Gaussian distribution or the normal distribution then please do watch this video all the way to the end.

X1, is distributed according to a Gaussian distribution, with some mean, which you want to write as mu1 and some variance, which I'm going to write as sigma squared 1, and so p of X1 is going to be a Gaussian probability distribution, with mean mu1 and variance sigma squared 1.

Gaussian density has a wider width.

This is a normal distribution, here's a cumulative distribution.

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