Thank you for this video. My question is which width will we consider as the value of σ^2 because when we move from the top of the bell to the bottom width increases.
Great question. This is something that confuses many people. The answer is that the "width" does not equal σ^2 at any particular "height"/"place" on the pdf plot. It is just drawn on the pdf plot to indicate that the width is a function of σ^2. From the equation for the pdf, we can see that as σ^2 increases, the width of the pdf "bell curve" gets wider (due to the term in the exponential), and the height of the curve gets lower (due to the factor out the front of the exponential). Overall, the area under the curve stays the same (ie. it equals 1, for all values of σ^2).
Yes, that's right. Brownian motion is modelled mathematically by the Wiener Process, which is a process that is almost surely continuous, and has independent increments that have a Gaussian distribution. In other words, it is the integral of a white noise Gaussian process.
The possible values are on the x-axis, and the probability density is on the y-axis. This video will hopefully help: "What is a Probability Density Function (pdf)?" th-cam.com/video/jUFbY5u-DMs/w-d-xo.html
If I was flipping a coin 100 times and counting how many tails came up. The x axis would represent the number of tails, the mid point would represent 50 tails representing the mean. My question is what does the y axis represent?
It represents the probability of getting that particular number (x) of tails. Perhaps it might help to watch this video: "What is a Probability Density Function (pdf)?" th-cam.com/video/jUFbY5u-DMs/w-d-xo.html
Neither! It's a French word, so you'll need to ask a French native speaker. We all do our best to pronounce words that are not derived from our native languages, but it's never the same as what it's really supposed to be.
Thanks man, had studied it in high school but forgot. Recently started machine learning and this helped a lot.
Glad I could help!
Very informative for people who is confusing the difference between PDF and Gaussian. Thank you so much
I'm glad it helped.
Thank you so much for making this video Sir, I am gonna share this one to my entire class
That's great to hear!
I need more elaboration on gaussian formula for probability distribution
very clear explanation !
I don’t need an hour. This is great and the accent is understandable. Sry peps
Thanks. I always try to explain things in a compact and clear way. I'm glad you were able to understand my Australian accent. 😅
in randomgaussian function what are the variables being changed?
1. How did they figure out this is the correct equation for a normal distribution?
2. How did Gauss find the integral?
A couple of interesting questions for a maths historian ...
maths historian here, gauss was just built different
Thank you for this video. My question is which width will we consider as the value of σ^2 because when we move from the top of the bell to the bottom width increases.
Great question. This is something that confuses many people. The answer is that the "width" does not equal σ^2 at any particular "height"/"place" on the pdf plot. It is just drawn on the pdf plot to indicate that the width is a function of σ^2. From the equation for the pdf, we can see that as σ^2 increases, the width of the pdf "bell curve" gets wider (due to the term in the exponential), and the height of the curve gets lower (due to the factor out the front of the exponential). Overall, the area under the curve stays the same (ie. it equals 1, for all values of σ^2).
@@iain_explains Thank you very much for this explanation :)
Glad it helped.
Come in handy as I’m studying theoretical music and how Tritone split the Octave in half and how we perceived harmonics.
Music is an interesting topic for signals analysis. I'm glad you found the video helpful.
Nice, thank you for this vid. Is it related to pdf of the fractional brownian motion?
Yes, that's right. Brownian motion is modelled mathematically by the Wiener Process, which is a process that is almost surely continuous, and has independent increments that have a Gaussian distribution. In other words, it is the integral of a white noise Gaussian process.
Good job ! I really like your explanation :)
Glad you liked it!
Good explanation sir . Can you explain the chi-square distribution?
Thanks for the suggestion. I've added it to my to-do list.
Here's the link to the video I made on the chi-square distribution: th-cam.com/video/B8QRtmT4I4g/w-d-xo.html
Thanks you for this explanation but I wanna know on which axes are gaussian distribution values??
The possible values are on the x-axis, and the probability density is on the y-axis. This video will hopefully help: "What is a Probability Density Function (pdf)?" th-cam.com/video/jUFbY5u-DMs/w-d-xo.html
What is the difference between the gaussian distribution and the normal distribution?
They are the same thing. Different people call it different things, that's all.
Explanation 🙌
Glad you liked it.
This is great, thank you
Glad you liked it!
thank you , Professor
You are very welcome
Very well explained 👍
Thank you 🙂
Great job Lain
Thanks.
If I was flipping a coin 100 times and counting how many tails came up. The x axis would represent the number of tails, the mid point would represent 50 tails representing the mean. My question is what does the y axis represent?
It represents the probability of getting that particular number (x) of tails. Perhaps it might help to watch this video: "What is a Probability Density Function (pdf)?" th-cam.com/video/jUFbY5u-DMs/w-d-xo.html
Fantastic. Thank you
Glad you liked it!
「あなたの動画はとても良いですし、メッセージがた
I'm glad you liked it.
Please can I give you a question to help me explain the answer?
Sure. What's your question?
gaussian just means normal distribution?
Yes
Thanks sir
You're welcome
All well and good as long as you on base economic projections or theories on it
Is it a Gow-see-in or Go-sh-un
Neither! It's a French word, so you'll need to ask a French native speaker. We all do our best to pronounce words that are not derived from our native languages, but it's never the same as what it's really supposed to be.
,very very larga negativ variable.” so x goes to value 0, but u r showing very very small values ( negativ) with ur pencil
Poor audio
1
nn