Changes between Version 6 and Version 7 of BysVecLinReg

Timestamp:

May 6, 2010, 5:16:39 PM (15 years ago)

Author:

Víctor de Buen Remiro

Comment:

--

BysVecLinReg

@@ -1 +1 @@
 = TOL Package BysVecLinReg = 
+
 
 BysVecLinReg yields for Bayesian simulator of Vectorial Linear Regression with 
 arbitrary constraining inequations.
 
-The method used in this package is based on [https://www.tol-project.org/export/HEAD/tolp/trunk/tol_pkg/BysVecLinReg/doc/bayes-linear-minka.pdf  Bayesian linear regression Thomas Minka (2001)] using invariant scale prior over [[LatexEquation(A)]] and inverse prior over [[LatexEquation(V)]]
+The method used to solve it in this package is based on 
+[https://www.tol-project.org/export/HEAD/tolp/trunk/tol_pkg/BysVecLinReg/doc/bayes-linear-minka.pdf 
+Bayesian linear regression Thomas Minka (2001)] using invariant scale prior over 
+[[LatexEquation(A)]] and inverse prior over [[LatexEquation(V)]]
+
+== Vectorial linear regression ==
 
 Vectorial linear regression equations are [[BR]]
@@ -36 +42 @@
 that will be used just to get more compact conditioninig expressions.
 
+== Arbitrary constraining inequations ==
+
 We will extend the model scope with arbitrary non null meassured restrictions 
 over parameters inside [[LatexEquation(A)]] by means of adding a set of 
@@ -48 +56 @@
 the arbitrary constraining function. [[BR]]
 
+== Invariant-scale prior over coefficient matrix ==
+
 Although Minka not explicitly stated in any place, under the invariant prior
 follows that [[LatexEquation(X)]] must be full-range [[LatexEquation(m <= N)]]
@@ -55 +65 @@
 forward to maximize the evidence of the data, which depends on the assumptions
 the model.
+
+== Inverse Wishart prior over covariance matrix ==
+
+...