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- Timestamp:
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May 6, 2010, 4:05:51 PM (15 years ago)
- Author:
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Víctor de Buen Remiro
- Comment:
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--
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v1
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| 1 | = BysVecLinReg = |
| 2 | |
| 3 | BysVecLinReg yields for Bayesian simulator of Vectorial Linear Regression with arbitrary constraining inequations |
| 4 | |
| 5 | Vectorial linear regression equations are [[BR]] |
| 6 | |
| 7 | [[LatexEquation(Y=A \cdot X + E)]] [[BR]] |
| 8 | |
| 9 | where [[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 | |
| 16 | All 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 | |
| 20 | where [[LatexEquation(V)]] is symmetric positive definite and unknown, but the same for each column[[BR]] |
| 21 | |
| 22 | When 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 | |
| 26 | being [[BR]] |
| 27 | |
| 28 | [[LatexEquation( F\left(A\right):\mathbb{R}^{d\times m}\longrightarrow\mathbb{R}^{r} )]] [[BR]] |
| 29 | |
| 30 | the constraining function. [[BR]] |
| 31 | |
| 32 | The 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)] |