User:Timothee Flutre/Notebook/Postdoc/2011/11/10
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(→Bayesian model of univariate linear regression for QTL detection: add what rest to be done) 
(→Bayesian model of univariate linear regression for QTL detection: fix typo + simplify) 

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<math>Y  X, \tau, B \sim \mathcal{N}(XB, \tau^{1} I_N)</math>  <math>Y  X, \tau, B \sim \mathcal{N}(XB, \tau^{1} I_N)</math>  
  Even though we can write the likelihood as a multivariate Normal, I still keep the term "univariate" in the title because the covariance matrix of <math>Y  X, \tau, B</math>  +  Even though we can write the likelihood as a multivariate Normal, I still keep the term "univariate" in the title because the covariance matrix of <math>Y  X, \tau, B</math> is in fact parametrized by a single real number, <math>\tau</math>. 
The likelihood of the parameters given the data is therefore:  The likelihood of the parameters given the data is therefore:  
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* '''Conditional posterior of B''':  * '''Conditional posterior of B''':  
  
  
<math>\mathsf{P}(B  Y, X, \tau) = \frac{\mathsf{P}(B, Y  X, \tau)}{\mathsf{P}(Y  X, \tau)}</math>  <math>\mathsf{P}(B  Y, X, \tau) = \frac{\mathsf{P}(B, Y  X, \tau)}{\mathsf{P}(Y  X, \tau)}</math>  
  +  Let's neglect the normalization constant for now:  
  +  
  +  
<math>\mathsf{P}(B  Y, X, \tau) \propto \mathsf{P}(B  \tau) \mathsf{P}(Y  X, \tau, B)</math>  <math>\mathsf{P}(B  Y, X, \tau) \propto \mathsf{P}(B  \tau) \mathsf{P}(Y  X, \tau, B)</math>  
  +  Similarly, let's keep only the terms in <math>B</math> for the moment:  
<math>\mathsf{P}(B  Y, X, \tau) \propto exp(B^T \Sigma_B^{1} B) exp((YXB)^T(YXB))</math>  <math>\mathsf{P}(B  Y, X, \tau) \propto exp(B^T \Sigma_B^{1} B) exp((YXB)^T(YXB))</math>  
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* '''Marginal posterior of B''': we can now integrate out <math>\tau</math>:  * '''Marginal posterior of B''': we can now integrate out <math>\tau</math>:  
  <math>\mathsf{P}(B  Y, X) = \int \mathsf{P}(  +  <math>\mathsf{P}(B  Y, X) = \int \mathsf{P}(\tau) \mathsf{P}(B  Y, X, \tau) \mathsf{d}\tau</math> 
<math>\mathsf{P}(B  Y, X) = \frac{\frac{\lambda^\ast}{2}^{\frac{N+\kappa}{2}}}{(2\pi)^\frac{3}{2} \Omega^{\frac{1}{2}} \Gamma(\frac{N+\kappa}{2})} \int \tau^{\frac{N+\kappa+3}{2}1} exp \left[\tau \left( \frac{\lambda^\ast}{2} + (B  \Omega X^TY)^T \Omega^{1} (B  \Omega X^TY) \right) \right] \mathsf{d}\tau</math>  <math>\mathsf{P}(B  Y, X) = \frac{\frac{\lambda^\ast}{2}^{\frac{N+\kappa}{2}}}{(2\pi)^\frac{3}{2} \Omega^{\frac{1}{2}} \Gamma(\frac{N+\kappa}{2})} \int \tau^{\frac{N+\kappa+3}{2}1} exp \left[\tau \left( \frac{\lambda^\ast}{2} + (B  \Omega X^TY)^T \Omega^{1} (B  \Omega X^TY) \right) \right] \mathsf{d}\tau</math> 
Revision as of 13:34, 22 November 2012
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Bayesian model of univariate linear regression for QTL detectionSee Servin & Stephens (PLoS Genetics, 2007).
where β_{1} is in fact the additive effect of the SNP, noted a from now on, and β_{2} is the dominance effect of the SNP, d = ak. Let's now write the model in matrix notation:
This gives the following multivariate Normal distribution for the phenotypes:
Even though we can write the likelihood as a multivariate Normal, I still keep the term "univariate" in the title because the covariance matrix of Y  X,τ,B is in fact parametrized by a single real number, τ. The likelihood of the parameters given the data is therefore:
A Gamma distribution for τ:
which means:
And a multivariate Normal distribution for B:
which means:
Let's neglect the normalization constant for now:
Similarly, let's keep only the terms in B for the moment:
We expand:
We factorize some terms:
Importantly, let's define:
We can see that Ω^{T} = Ω, which means that Ω is a symmetric matrix. This is particularly useful here because we can use the following equality: Ω^{ − 1}Ω^{T} = I.
This now becomes easy to factorizes totally:
We recognize the kernel of a Normal distribution, allowing us to write the conditional posterior as:
Similarly to the equations above:
But now, to handle the second term, we need to integrate over B, thus effectively taking into account the uncertainty in B:
Again, we use the priors and likelihoods specified above (but everything inside the integral is kept inside it, even if it doesn't depend on B!):
As we used a conjugate prior for τ, we know that we expect a Gamma distribution for the posterior. Therefore, we can take τ^{N / 2} out of the integral and start guessing what looks like a Gamma distribution. We also factorize inside the exponential:
We recognize the conditional posterior of B. This allows us to use the fact that the pdf of the Normal distribution integrates to one:
We finally recognize a Gamma distribution, allowing us to write the posterior as:
where
Here we recognize the formula to integrate the Gamma function:
And we now recognize a multivariate Student's tdistribution:
We hence can write:
invariance properties motivate the use of limits for some "unimportant" hyperparameters average BF over grid
