User:Timothee Flutre/Notebook/Postdoc/2011/11/10
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(→Bayesian model of univariate linear regression for QTL detection: fix typo + simplify) 
(→Bayesian model of univariate linear regression for QTL detection: fix typo sign lambda*) 

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This allows us to use the fact that the pdf of the Normal distribution integrates to one:  This allows us to use the fact that the pdf of the Normal distribution integrates to one:  
  <math>\mathsf{P}(\tau  Y, X) \propto \tau^{\frac{N+\kappa}{2}  1} e^{\frac{\lambda}{2} \tau} exp\left[\frac{\tau}{2} (Y^T X \Omega X  +  <math>\mathsf{P}(\tau  Y, X) \propto \tau^{\frac{N+\kappa}{2}  1} e^{\frac{\lambda}{2} \tau} exp\left[\frac{\tau}{2} (Y^T Y  Y^T X \Omega X^T Y) \right]</math> 
We finally recognize a Gamma distribution, allowing us to write the posterior as:  We finally recognize a Gamma distribution, allowing us to write the posterior as:  
  <math>\tau  Y, X \sim \Gamma \left( \frac{N+\kappa}{2}, \; \frac{1}{2} (Y^T X \Omega X  +  <math>\tau  Y, X \sim \Gamma \left( \frac{N+\kappa}{2}, \; \frac{1}{2} (Y^T Y  Y^T X \Omega X^T Y + \lambda) \right)</math> 
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<math>B, \tau  Y, X \sim \mathcal{N}IG(\Omega X^TY, \; \tau^{1}\Omega, \; \frac{N+\kappa}{2}, \; \frac{\lambda^\ast}{2})</math>  <math>B, \tau  Y, X \sim \mathcal{N}IG(\Omega X^TY, \; \tau^{1}\Omega, \; \frac{N+\kappa}{2}, \; \frac{\lambda^\ast}{2})</math>  
  where <math>\lambda^\ast =  +  where <math>\lambda^\ast = Y^T Y  Y^T X \Omega X^T Y + \lambda</math> 
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We hence can write:  We hence can write:  
  <math>B  Y, X \sim \mathcal{S}_{N+\kappa}(\Omega X^TY, \; (Y^T X \Omega X  +  <math>B  Y, X \sim \mathcal{S}_{N+\kappa}(\Omega X^TY, \; (Y^T Y  Y^T X \Omega X^T Y + \lambda) \Omega)</math> 
Revision as of 12:55, 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
