What is Fisher Information?
ฝัง
- เผยแพร่เมื่อ 12 มิ.ย. 2022
- Explains the concept of Fisher Information in relation to statistical estimation of parameters based on random measurements. Gives an example of parameter estimation in Gaussian noise, and shows the component functions to help with providing intuition.
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Love the format of the video. Emphasising the intuition just makes everything else clearer
Glad you enjoyed it!
Great video! I have been following your content for quite a while now and you really always try to give the intuition behind the each process. This really helps to understand the material :) Much appreciated!!!
I'm so glad to hear that you like the videos, and find the intuition helpful.
This is really a great explanation. You made a difficult concept (at least for me) very easy to understand. I’ve been watching other videos with animations and all, but I only understood this well after watching your explanation. Thank you very much.
That's so great to hear. I'm glad it was helpful!
What a wonderful coincidence! Here I am deriving a CRB for a noise variance model I'm researching, and running MLE simulations to verify it, and your video with this great explanation of FI comes up :-). I've read that the multivariate FIM can be considered a metric in the parameter space, and with your explanation of how the derivative takes into account the variation of the PDF wrt to the parameter, I can almost visualize it for an intuitive understanding - fascinating concepts. Thanks Iain!
I'm so glad it was helpful. The concept is not particularly intuitive, especially when considering the multivariate case with the FIM. Perhaps the FIM can be the topic of a future video.
Im a visual learner the graphs really helped! but I almost gave up half way through the video but I'm glad I hung on!
I'm glad you found the graphs helpful.
Thank you very much for this amazing video! You made understanding this concept very easy.
Great video to understand the Fisher Information intuitively. Thank you Prof. Iain.
Glad it was helpful!
Thank you for explaining the logic behind that formula. Knowing the why helps me remember easily and just makes it all make sense.
That's great to hear. I'm glad you found the video helpful.
Thank you for the video. Love the way you used simple examples to explain the theory intuitively and decomposed the expression explaining the meaning of each part!
Glad it was helpful!
Thanks a lot. This really provides a excellent complementary explanation to S. Kay's book. In the book Fisher Information was interpreted as the 'curvature of the log-likelihood function', where the expectation of squared 1st derivative can be converted to negative of expectation of 2nd derivative, and f is viewed as a function of theta with Y fixed. The meaning of natural log will become more subtle when it comes to the derivation of CRLB.
Glad it was helpful! It's a concept that took me a long time to get intuition on, when I first learned about it.
Awesome video! You concentrate on giving the intuiton, thanks!
I'm glad you liked it.
Thanks a lot for your intuitive explanation with diagrams. The explanation about why Fisher Information looks like that is quite useful to understand the definition!
That's great to hear. I'm glad you liked the video.
Beautifully explained ! Thank you
You are welcome!
I appreciate your work Mr. lain, it is very helpful, it gives great sense of the signals and systems.
Glad it was helpful!
Thank you so much, I was literature research for some novel Cramer rao lower bound application. You video helped me a lot!
I'm so glad!
Absolutely great explanation! made my my life easy.
Glad you liked it!
@@iain_explains Thank you for that.
Hey Ian, great content. Have you considered doing a video on the Cramer-Rao bound (and how it relates to Fisher information)? I was thinking of doing some statistical signal processing videos on my channel but you've covered so much already haha.
Thanks for the suggestion. Yes, I've thought about it. It's the inverse of the Fisher information, but there's really not much intuition as to why this is the case - except that the maths turns out that way.
The most informative video on Fisher information I have seen, pun intended! 😄
😉
讲的真好，谢谢了
I'm glad you liked it.
The beauty of math becomes evident when a good teacher teaches it :) ... thank you! thank you!
You're very welcome!
Amazing... Thank you professor. In the litterature there is rare clear book on the subject beside Pr Steve Mc Kay collection.
Glad it was helpful!
Great video thanks !
Glad you liked it!
Excellent!
Glad you liked it!
Excellent video thank you sir.
Glad it was helpful!
Wonderful!!!!
Glad you like it!
I'm a little confused as to why we take the log. Specifically, wouldn't we want the part of the function that changes the most to have more weight in the expectation? Aren't small changes not that notable in comparison?
Very informative video. The normal distribution is plotted for a particular value of the mean. How can we perform differentiation wrt to the mean? Am I missing something here.
In the definition of Fisher Information, there is a log function. This cancels out the exponential function in the function f(y;theta).
i am from india ........ your video is so informative
I'm glad you found it helpful.
Thanks. Is the FI only useful when you compare a situation with the identical PDF?
There don't seem to be an unit for the FI, or can you compare parameter with different PDFs?
Just to clarify, the FI is not "comparing situations". It is a measure of the information in a random variable drawn from a (single) PDF. Of course the FI measure for different random variables (PDFs) can be compared.
@@iain_explains Yes, I just thought, you get a number for the FI. But for what purpose? Without the unit you cannot put the number in a relation. Now I read that they use that value for the Cramer-Rao bound.
Example: like the uniform distribution, the FI is zero, and this is why they call the Uniform PDF: "uninformative PDF". So FI is a helpful measure even outside the use of Cramer-Rao bound calculation.
Thank you sir
Welcome
great video thank you! what about fisher information and CRLB?
Yes, thanks for the suggestion. It's on my "to do" list.
Also in the video we never talked about which function gives most info about theta? is it pills , coffee or arm?
absolutely made everything understandable, better than my college professor😁
Great. I'm glad you found it useful.
Am I a dummy? For the fisher info of theta I have computed theta/(pop variance). (I have theta instead of 1)
sir, can you please tell the difference between
1.Maximum likelihood (ML)
2.Maximum Aposteriori (MAP)
3.Least squares (LS)
4.Minimum mean square error (MMSE)
5. zero forcing (ZF).
moreover, are the equalizer and receiver the same?
if possible, please post a video on this topic sir. Thank you so much for inspiring us sir.
Have you checked out my webpage? iaincollings.com I've already got videos on all the topics you ask about. There are lots of them, but the three most relevant would be: "What are Maximum Likelihood (ML) and Maximum a posteriori (MAP)?" th-cam.com/video/9Ahdh_8xAEI/w-d-xo.html and "How are Matched Filter (MF), Zero Forcing (ZF), and MMSE Related?" th-cam.com/video/U3qjVgX2poM/w-d-xo.html and "What is Least Squares Estimation?" th-cam.com/video/BZ9VlmmuotM/w-d-xo.html
@@iain_explains Thank you so much sir.you have taken time to give me reply and all the links. Thank you so much agin sir.
🎯 Key Takeaways for quick navigation:
00:01 🌡️ *Fisher Information measures information content in measurements about a parameter. An example is given with various methods to measure stomach temperature.*
03:23 📊 *The Fisher Information formula is explained, involving the expected value of the squared derivative of the log of the probability density function (pdf) of a random variable.*
07:22 📉 *Fischer Information is inversely proportional to the variance of noise in measurements. Small noise leads to high information, while large noise results in low information.*
14:22 🔄 *The log function in the Fisher Information formula enhances small values in the pdf, ensuring contributions from all parts of the distribution, giving a comprehensive measure.*
16:50 📈 *Fischer Information decreases as noise (standard deviation) increases, illustrated with visualizations of pdf changes and their impact on the information measure.*
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Could you kindly explain "what is polarization " of polar code :) ?
I'll have to give that one some more thought. I don't really have a good intuitive explanation right now.
@@iain_explains Thank you professor
12:00
i wish you were my professor
That's nice of you to say. I'm glad you like the videos.
mistake at 15:07, fisher information would be 16 as 1/(0.25)^2=16
No, you're mistaken. I said the variance is 0.25. The symbol for variance is sigma^2.
@@iain_explains ok thanks my bad
Hi, I really enjoy your videos, and I have learned a lot. I tried to find your email to contact you, and I only find your University's email. I am a Ph.D. student, and I am working on a D2D base on the NOMA system. So, could you please explain the D2D system and how it works? Thank you
Thanks for the suggestion. I'll put it on my "to do" list.