Maximum a posteriori, type-II error











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I've been trying to derive the marginalized likelihood, which I think I have done successfully. The problem is that I've ended up with an expression that I can't really explain, my expression is like this:



$$L(W) = -frac{1}{2}sum_{i=1}^{N}y_i^{T}(WW^{T} + sigma^{2}I)^{-1}y_i$$



The problem is to explain the terms, for the "normal" Maximum Likelihood as well as the MAP, it's fairly straight forward, but I have a hard time understanding what this really means, is there anyone who can provide and explanation?






maximum-likelihood







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    up vote
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    down vote

    favorite












    I've been trying to derive the marginalized likelihood, which I think I have done successfully. The problem is that I've ended up with an expression that I can't really explain, my expression is like this:



    $$L(W) = -frac{1}{2}sum_{i=1}^{N}y_i^{T}(WW^{T} + sigma^{2}I)^{-1}y_i$$



    The problem is to explain the terms, for the "normal" Maximum Likelihood as well as the MAP, it's fairly straight forward, but I have a hard time understanding what this really means, is there anyone who can provide and explanation?






    maximum-likelihood







    share|cite|improve this question
























      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      I've been trying to derive the marginalized likelihood, which I think I have done successfully. The problem is that I've ended up with an expression that I can't really explain, my expression is like this:



      $$L(W) = -frac{1}{2}sum_{i=1}^{N}y_i^{T}(WW^{T} + sigma^{2}I)^{-1}y_i$$



      The problem is to explain the terms, for the "normal" Maximum Likelihood as well as the MAP, it's fairly straight forward, but I have a hard time understanding what this really means, is there anyone who can provide and explanation?






      maximum-likelihood







      share|cite|improve this question













      I've been trying to derive the marginalized likelihood, which I think I have done successfully. The problem is that I've ended up with an expression that I can't really explain, my expression is like this:



      $$L(W) = -frac{1}{2}sum_{i=1}^{N}y_i^{T}(WW^{T} + sigma^{2}I)^{-1}y_i$$



      The problem is to explain the terms, for the "normal" Maximum Likelihood as well as the MAP, it's fairly straight forward, but I have a hard time understanding what this really means, is there anyone who can provide and explanation?






      maximum-likelihood




      maximum-likelihood






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      A.Maine

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