Lawrence Leemis
Lawrence Leemis
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Moment generating function technique -- Example 2
Moment generating function technique -- Example 2
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Video

Moment generating function technique -- Example 13:10Moment generating function technique -- Example 1
Moment generating function technique -- Example 1
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Moment generating function technique Example 1
Moment generating function technique4:20Moment generating function technique
Moment generating function technique
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Moment generating function technique
Order statistics -- Example 711:25Order statistics -- Example 7
Order statistics -- Example 7
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Order statistics Example 7
Order statistics -- Example 65:07Order statistics -- Example 6
Order statistics -- Example 6
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Order statistics Example 6
Order statistics special cases2:28Order statistics special cases
Order statistics special cases
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Order statistics special cases
Order statistics marginal distributions result8:10Order statistics marginal distributions result
Order statistics marginal distributions result
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Order statistics marginal distributions result
Order statistics -- Example 54:42Order statistics -- Example 5
Order statistics -- Example 5
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Order statistics Example 5
Order statistics -- Example 43:08Order statistics -- Example 4
Order statistics -- Example 4
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Order statistics Example 4
Order Statistics -- Example 33:00Order Statistics -- Example 3
Order Statistics -- Example 3
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Order Statistics Example 3
Order statistics joint distribution result1:59Order statistics joint distribution result
Order statistics joint distribution result
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Order statistics joint distribution result
Order statistics -- Example 24:50Order statistics -- Example 2
Order statistics -- Example 2
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Order statistics Example 2
Order statistics -- Example 112:36Order statistics -- Example 1
Order statistics -- Example 1
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Order statistics Example 1
Order statistics1:59Order statistics
Order statistics
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Order statistics
Transformation technique for bivariate continuous random variables -- Example 32:17Transformation technique for bivariate continuous random variables -- Example 3
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 23:10Transformation technique for bivariate continuous random variables -- Example 2
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 113:15Transformation technique for bivariate continuous random variables -- Example 1
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 variables2:16Transformation technique for bivariate continuous random variables
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 114:57Transformation technique for bivariate discrete random variables -- Example 1
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 variables2:03Transformation technique for bivariate discrete random variables
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 110:04Transformation technique for continuous random variables -- Example 1
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 variables4:19Transformation technique for continuous random variables
Transformation technique for continuous random variables
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Transformation technique for continuous random variables
Transformation technique for discrete random variables -- Example 17:41Transformation technique for discrete random variables -- Example 1
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 variables4:41Transformation technique for discrete random variables
Transformation technique for discrete random variables
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Transformation technique for discrete random variables
Cumulative distribution technique -- Example 412:17Cumulative distribution technique -- Example 4
Cumulative distribution technique -- Example 4
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Cumulative distribution technique Example 4
Cumulative distribution technique -- Example 312:14Cumulative distribution technique -- Example 3
Cumulative distribution technique -- Example 3
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Cumulative distribution technique Example 3
Cumulative distribution technique -- Example 210:37Cumulative distribution technique -- Example 2
Cumulative distribution technique -- Example 2
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Cumulative distribution technique Example 2
Cumulative distribution technique -- Example 15:15Cumulative distribution technique -- Example 1
Cumulative distribution technique -- Example 1
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Cumulative distribution technique Example 1
Chapter 7 roadmap8:04Chapter 7 roadmap
Chapter 7 roadmap
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Chapter 7 roadmap
Cumulative distribution function technique4:51Cumulative distribution function technique
Cumulative distribution function technique
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Cumulative distribution function technique

Komentáře

  • @DanielTitanz
    @DanielTitanz Před 2 měsíci

    Who's here in 2024 with me😂

  • @roshangeorge97
    @roshangeorge97 Před 3 měsíci

    it was aksed in this year gate DA paper.. thanks for posting!

  • @kellyprowse8362
    @kellyprowse8362 Před 4 měsíci

    Thank you thank you thank you :)))

  • @hungwaikay7747
    @hungwaikay7747 Před 4 měsíci

    After probability videos, it would be good to continue with statistics videos...

  • @TylerRaffaele-gd4hz
    @TylerRaffaele-gd4hz Před 4 měsíci

    this video sucks

  • @hungwaikay7747
    @hungwaikay7747 Před 4 měsíci

    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))...?

  • @metasameta236
    @metasameta236 Před 5 měsíci

    I promise you’re the best 🙌

  • @metasameta236
    @metasameta236 Před 5 měsíci

    please please make more statistics videos

  • @metasameta236
    @metasameta236 Před 5 měsíci

    Your videos are so hepful. Can't thank you enough

  • @trendcools30
    @trendcools30 Před 5 měsíci

    Next time try something that doesn't scare the viewer coz wtf😂😂FR though

  • @ianwaweru5299
    @ianwaweru5299 Před 6 měsíci

    Are you still posting. I absolutely love this

  • @HZ-eo1dy
    @HZ-eo1dy Před 7 měsíci

    great video

  • @lonesailor6175
    @lonesailor6175 Před 9 měsíci

    Thank you

  • @mdabuzihad2000
    @mdabuzihad2000 Před 10 měsíci

    Thanks Sir

  • @cypherecon5989
    @cypherecon5989 Před 11 měsíci

    Hello where can I get your textbook/ script?

    • @lawrenceleemis9944
      @lawrenceleemis9944 Před 8 měsíci

      The book is titled "Probability" and is available on Amazon, Barnes & Noble, etc.

    • @lawrenceleemis9944
      @lawrenceleemis9944 Před 8 měsíci

      Here is a link: www.amazon.com/Probability-Lawrence-M-Leemis/dp/0982917473

  • @vikasyadav9345
    @vikasyadav9345 Před rokem

    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

  • @monicaselesmuwaya9815
    @monicaselesmuwaya9815 Před rokem

    Wonderful lecture but sir what book are you using

    • @lawrenceleemis9944
      @lawrenceleemis9944 Před 8 měsíci

      The book is titled "Probability" and is available on Amazon, Barnes & Noble, etc.

    • @lawrenceleemis9944
      @lawrenceleemis9944 Před 8 měsíci

      Link: www.amazon.com/Probability-Lawrence-M-Leemis/dp/0982917473

  • @akanmuazeez1154
    @akanmuazeez1154 Před rokem

    Thank you for the well detailed explanation. Please what's the name of the book you are referring to?

    • @lawrenceleemis9944
      @lawrenceleemis9944 Před rokem

      Probability. There are some sample pages given at www.math.wm.edu/~leemis.

  • @akarshanmishra2351
    @akarshanmishra2351 Před rokem

    Thank you so much. This helped a ton in my review!

  • @denizkaragullu6239
    @denizkaragullu6239 Před rokem

    Thank you so much you saved me

  • @odelolatechup1447
    @odelolatechup1447 Před rokem

    You didn't explain the topics, I don't get

    • @oscarreneduarte2478
      @oscarreneduarte2478 Před rokem

      for real this video is trash

    • @shambo9807
      @shambo9807 Před 11 měsíci

      There's a whole long playlist with all the video explanations and multiple examples

  • @kuhajeyangunaratnam8652
    @kuhajeyangunaratnam8652 Před rokem

    if you can compile a playlist for multivariate analyis it would greatly help

  • @rigsteel1966
    @rigsteel1966 Před rokem

    Love this video! Thank you so much!

  • @rigsteel1966
    @rigsteel1966 Před rokem

    So grateful for information like this!

  • @oanhkhuyen
    @oanhkhuyen Před rokem

    Thank you so much

  • @deveshnandan323
    @deveshnandan323 Před 2 lety

    Simply Awesome :)

  • @jorgeafb3938
    @jorgeafb3938 Před 2 lety

    Hi! Could you recommend me a bibliography where I can find this procedure and more detail? Thank you!

  • @crisearlbalangyao3190
    @crisearlbalangyao3190 Před 2 lety

    What's the final solution please?

  • @enasgirave5773
    @enasgirave5773 Před 2 lety

    Thanks

  • @fariaraisa2072
    @fariaraisa2072 Před 2 lety

    Do you know any distribution except geometric distribution 😡😡😡

  • @bernardkumi7964
    @bernardkumi7964 Před 2 lety

    Great work 👍

  • @spyhunter0066
    @spyhunter0066 Před 2 lety

    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???

  • @spyhunter0066
    @spyhunter0066 Před 2 lety

    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.

  • @Grassmpl
    @Grassmpl Před 2 lety

    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)?

  • @hengzhou4566
    @hengzhou4566 Před 2 lety

    Sec 3.5.2., page 204-205, "Introduction to Mathematical Statistics", 8th edition, Robert V. Hogg et al.

  • @gehadmohamed9751
    @gehadmohamed9751 Před 2 lety

    thanks sir

  • @irenenyaguthii2573
    @irenenyaguthii2573 Před 2 lety

    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

  • @elijahflorence4959
    @elijahflorence4959 Před 2 lety

    420 blaze

  • @akhil_g
    @akhil_g Před 2 lety

    Sir,please refer me book for more numerical questions on transformation (discrete+continuous)🙏

    • @lawrenceleemis9944
      @lawrenceleemis9944 Před rokem

      Chapter 7 in second edition of "Probability" book (www.math.wm.edu/~leemis)

  • @submarine1839
    @submarine1839 Před 2 lety

    Helpful 🙏

  • @jessamaelastimoso1448
    @jessamaelastimoso1448 Před 2 lety

    Thank you, Laurence.

  • @gillespieQMA
    @gillespieQMA Před 2 lety

    Thank you so much! helped me greatly on my graduate level probability assignment!

  • @jorsch4689
    @jorsch4689 Před 2 lety

    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.

  • @IKE-kf7cg
    @IKE-kf7cg Před 2 lety

    Can you give me book

  • @nishatjahan1781
    @nishatjahan1781 Před 3 lety

    Great explanation 😇

  • @user-or7ji5hv8y
    @user-or7ji5hv8y Před 3 lety

    This is so well explained.

  • @jordanakhan7413
    @jordanakhan7413 Před 3 lety

    Hi, which textbook do you use for this?

    • @3a146
      @3a146 Před 3 lety

      His own text

  • @Songvbm
    @Songvbm Před 3 lety

    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?

    • @Elegia-rh2uh
      @Elegia-rh2uh Před 3 lety

      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.

  • @poetryartandmusic6575
    @poetryartandmusic6575 Před 3 lety

    can you share those notes

  • @paulooctavioaraujo3087
    @paulooctavioaraujo3087 Před 3 lety

    Why the first (x - μ) that appears in the exponential is transposed and the second one is not?

    • @sierramaesorongon6793
      @sierramaesorongon6793 Před 2 lety

      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