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Why is the likelihood for bayesian liner regression in that form?



Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern)Queries on the Bayesian methodAssessing goodness of fit in Bayesian frameworkConfusion about Notation for Bayesian StatisticsBayesian information criterion derivation for linear regressionUsing the linear regression equation in the Bayesian Model Averaging with RHow to optimize the log likelihood to obtain parameters for the maximum likelihood estimate?Bayesian Regression Simplifying the posteriorBayesian Interpretation for Ridge Regression and the LassoBayesian Linear RegressionWhy Bayesian Approach don't use test data for model validation?










0












$begingroup$


Let's say we have a linear model $Y = bX + e$ where $b$ represents the parameter.



Normally, the posterior is written like this for data $X_n$ and $Y_n: P(B|Y_n, X_n) = P(Y_n|X_n,b)P(b|X_n)/P(Y_n|X_n)$



Also, look at the following equation where D represents the data and L represents the parameters. The posterior is normally written like $P(L|D) = P(D|L)P(L)/P(D)$.



But if you do a direct substitution where $D = X_n,Y_n$ and $L = B$ you get:
$P(B|X_n,Y_n) = P(Y_n,X_n|b)P(b)/P(Y_n,X_n)$



Why is the second equation not used? What am I missing?










share|cite|improve this question











$endgroup$
















    0












    $begingroup$


    Let's say we have a linear model $Y = bX + e$ where $b$ represents the parameter.



    Normally, the posterior is written like this for data $X_n$ and $Y_n: P(B|Y_n, X_n) = P(Y_n|X_n,b)P(b|X_n)/P(Y_n|X_n)$



    Also, look at the following equation where D represents the data and L represents the parameters. The posterior is normally written like $P(L|D) = P(D|L)P(L)/P(D)$.



    But if you do a direct substitution where $D = X_n,Y_n$ and $L = B$ you get:
    $P(B|X_n,Y_n) = P(Y_n,X_n|b)P(b)/P(Y_n,X_n)$



    Why is the second equation not used? What am I missing?










    share|cite|improve this question











    $endgroup$














      0












      0








      0





      $begingroup$


      Let's say we have a linear model $Y = bX + e$ where $b$ represents the parameter.



      Normally, the posterior is written like this for data $X_n$ and $Y_n: P(B|Y_n, X_n) = P(Y_n|X_n,b)P(b|X_n)/P(Y_n|X_n)$



      Also, look at the following equation where D represents the data and L represents the parameters. The posterior is normally written like $P(L|D) = P(D|L)P(L)/P(D)$.



      But if you do a direct substitution where $D = X_n,Y_n$ and $L = B$ you get:
      $P(B|X_n,Y_n) = P(Y_n,X_n|b)P(b)/P(Y_n,X_n)$



      Why is the second equation not used? What am I missing?










      share|cite|improve this question











      $endgroup$




      Let's say we have a linear model $Y = bX + e$ where $b$ represents the parameter.



      Normally, the posterior is written like this for data $X_n$ and $Y_n: P(B|Y_n, X_n) = P(Y_n|X_n,b)P(b|X_n)/P(Y_n|X_n)$



      Also, look at the following equation where D represents the data and L represents the parameters. The posterior is normally written like $P(L|D) = P(D|L)P(L)/P(D)$.



      But if you do a direct substitution where $D = X_n,Y_n$ and $L = B$ you get:
      $P(B|X_n,Y_n) = P(Y_n,X_n|b)P(b)/P(Y_n,X_n)$



      Why is the second equation not used? What am I missing?







      statistics regression bayesian






      share|cite|improve this question















      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited Mar 27 at 15:31









      YuiTo Cheng

      2,54841037




      2,54841037










      asked Mar 27 at 15:29









      Anirudh SharmaAnirudh Sharma

      11




      11




















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