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
asked Nov 19 at 15:17
A.Maine
398
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up vote
0
down vote
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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
asked Nov 19 at 15:17
A.Maine
398
add a comment |
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
asked Nov 19 at 15:17
A.Maine
398
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
asked Nov 19 at 15:17
A.Maine
398
asked Nov 19 at 15:17
A.Maine
398
asked Nov 19 at 15:17
A.Maine
398
asked Nov 19 at 15:17
A.Maine
398
asked Nov 19 at 15:17
A.Maine
398
398
add a comment |
add a comment |
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