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Changes between Initial Version and Version 1 of BysVecLinReg


Ignore:
Timestamp:
May 6, 2010, 4:05:51 PM (15 years ago)
Author:
Víctor de Buen Remiro
Comment:

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  • BysVecLinReg

    v1 v1  
     1= BysVecLinReg =
     2
     3BysVecLinReg yields for Bayesian simulator of Vectorial Linear Regression with arbitrary constraining inequations
     4
     5Vectorial linear regression equations are [[BR]]
     6
     7[[LatexEquation(Y=A \cdot X + E)]] [[BR]]
     8
     9where [[BR]]
     10
     11 * [[LatexEquation(Y\in\mathbb{R}^{d\times N} )]] is the multivariant known output matrix, where each row is a different output vector [[LatexEquation(y_{n}\in\mathbb{R}^{N} )]]  [[BR]]
     12 * [[LatexEquation(X\in\mathbb{R}^{m\times N} )]] is the known and full rank input matrix, where each row is a different input vector [[LatexEquation(x_{n}\in\mathbb{R}^{N} )]] [[BR]]
     13 * [[LatexEquation(A\in\mathbb{R}^{d\times m} )]] has the unknown regression coefficients that we want to estimate [[BR]]
     14 * [[LatexEquation(E\in\mathbb{R}^{d\times N} )]] is the multivariant residuals, where each row is the residuals vector  [[LatexEquation(e_{n}\in\mathbb{R}^{N} )]] corresponding to output [[LatexEquation(y_{n} )]]
     15 
     16All residuals inside the same row are incorrelated normal, but resiudals in the same column [[LatexEquation(j)]] are [[BR]]
     17
     18[[LatexEquation(e_{.,j} \sim N\left(0,V\right) E\in\mathbb{R}^{d\times d} \forall j=1 \ldots d )]][[BR]]
     19
     20where [[LatexEquation(V)]] is symmetric positive definite and unknown, but the same for each column[[BR]]
     21
     22When there are some restriction over parameters inside [[LatexEquation(A)]] we must to add the inequations of feasible region [[BR]]
     23
     24[[LatexEquation(\Omega = \left\{ A\in\mathbb{R}^{d\times m} \mid F\left(A\right) \le 0 \right\})]] [[BR]]
     25
     26being [[BR]]
     27
     28[[LatexEquation( F\left(A\right):\mathbb{R}^{d\times m}\longrightarrow\mathbb{R}^{r} )]]  [[BR]]
     29
     30the constraining function. [[BR]]
     31
     32The method used in this package is based on [http://research.microsoft.com/en-us/um/people/minka/papers/minka-linear.ps.gz  Bayesian linear regression Thomas Minka (2001)]