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Lawrence Leemis
Registrace 24. 12. 2014
Moment generating function technique -- Example 2
Moment generating function technique -- Example 2
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Video
Moment generating function technique -- Example 1
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Moment generating function technique Example 1
Order statistics marginal distributions result
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Order statistics marginal distributions result
Order statistics joint distribution result
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Order statistics joint distribution result
Transformation technique for bivariate continuous random variables -- Example 3
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Transformation technique for bivariate continuous random variables Example 3
Transformation technique for bivariate continuous random variables -- Example 2
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Transformation technique for bivariate continuous random variables Example 2
Transformation technique for bivariate continuous random variables -- Example 1
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Transformation technique for bivariate continuous random variables Example 1
Transformation technique for bivariate continuous random variables
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Transformation technique for bivariate continuous random variables
Transformation technique for bivariate discrete random variables -- Example 1
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Transformation technique for bivariate discrete random variables Example 1
Transformation technique for bivariate discrete random variables
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Transformation technique for bivariate discrete random variables
Transformation technique for continuous random variables -- Example 1
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Transformation technique for continuous random variables Example 1
Transformation technique for continuous random variables
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Transformation technique for continuous random variables
Transformation technique for discrete random variables -- Example 1
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Transformation technique for discrete random variables Example 1
Transformation technique for discrete random variables
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Transformation technique for discrete random variables
Cumulative distribution technique -- Example 4
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Cumulative distribution technique Example 4
Cumulative distribution technique -- Example 3
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Cumulative distribution technique Example 3
Cumulative distribution technique -- Example 2
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Cumulative distribution technique Example 2
Cumulative distribution technique -- Example 1
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Cumulative distribution technique Example 1
Cumulative distribution function technique
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Cumulative distribution function technique
Who's here in 2024 with me😂
it was aksed in this year gate DA paper.. thanks for posting!
Thank you thank you thank you :)))
After probability videos, it would be good to continue with statistics videos...
this video sucks
Confused as why (n * (2/(2n-1))) is valid? (2/(2n-1)) is valid for first couple, shouldn't the remaining couples is (2/(2n-3)), (2/(2n-5)), (2/(2n-7))...?
I promise you’re the best 🙌
please please make more statistics videos
Your videos are so hepful. Can't thank you enough
Next time try something that doesn't scare the viewer coz wtf😂😂FR though
Are you still posting. I absolutely love this
great video
Thank you
Thanks Sir
Hello where can I get your textbook/ script?
The book is titled "Probability" and is available on Amazon, Barnes & Noble, etc.
Here is a link: www.amazon.com/Probability-Lawrence-M-Leemis/dp/0982917473
Thank you so much ,this video is very useful because tomorrow is my presentation and I am so nervous but still waching this video then I am be confident for giving presentation again thank you so much
Wonderful lecture but sir what book are you using
The book is titled "Probability" and is available on Amazon, Barnes & Noble, etc.
Link: www.amazon.com/Probability-Lawrence-M-Leemis/dp/0982917473
Thank you for the well detailed explanation. Please what's the name of the book you are referring to?
Probability. There are some sample pages given at www.math.wm.edu/~leemis.
Thank you so much. This helped a ton in my review!
Thank you so much you saved me
You didn't explain the topics, I don't get
for real this video is trash
There's a whole long playlist with all the video explanations and multiple examples
if you can compile a playlist for multivariate analyis it would greatly help
Love this video! Thank you so much!
So grateful for information like this!
Thank you so much
Simply Awesome :)
Hi! Could you recommend me a bibliography where I can find this procedure and more detail? Thank you!
What's the final solution please?
Thanks
Do you know any distribution except geometric distribution 😡😡😡
www.math.wm.edu/~leemis/chart/UDR/UDR.html
@@lawrenceleemis9944 Haha, nice link
Great work 👍
The thing I cant get it in the beginning is how two x1 and x2 data set creates mu1 and mu2 separetely. One Gaussian shape data set should have only one mean and sigma, right???
How do you write the likelihood for a multivariate Gaussian distribution with p correlation factor? Plus, imagine your data set is constructed by counts per channels as Gaussian shape histogram. Thanks.
How does the geometric proof account for the f(*) factor? This is not probability. The RVs are continuous so f(*) needs to be obtained by taking derivatives. How do you know the derivative works out with the fixed observation x_(k)?
Sec 3.5.2., page 204-205, "Introduction to Mathematical Statistics", 8th edition, Robert V. Hogg et al.
thanks sir
By appealing to the circular symmetry of the standard bivariate normal distribution, show how samples from a Cauchy distribution could be generated from independent N(0,1) samples
420 blaze
Sir,please refer me book for more numerical questions on transformation (discrete+continuous)🙏
Chapter 7 in second edition of "Probability" book (www.math.wm.edu/~leemis)
Helpful 🙏
Thank you, Laurence.
Thank you so much! helped me greatly on my graduate level probability assignment!
Better than what my professor gave. Here's how he defined it: "Let X and Y be two random variables. We denote by E(X|Y = y) the expected value of the conditional distribution of X given Y = y. The conditional expectation of X given Y is denoted by E(X|Y ) and is defined to be E(X|Y = y) on the event Y = y." Kind of circular if you ask me.
Can you give me book
Great explanation 😇
This is so well explained.
Hi, which textbook do you use for this?
His own text
I am new in Statistics and what I need to understand may sound of very basic level. Yet I want to know it. On 0:02 why there are so many normal curves from x-axis and y axis? What do those curves signify?
Because this is a 3D graph as opposed to a 2D one. This is on an x, y, z plane as opposed to just an x, y plane.
can you share those notes
Why the first (x - μ) that appears in the exponential is transposed and the second one is not?
we r talking about multivariate so, that sigma inverse is in matrix notation premultiply by a vector(x-mu), recall that in linear algebra we can just multiply if they are conformable. inorder to do so we need to transpose x-mu and the other one as is x-mu without transpose