Some formulas in the documentation
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@ -43,7 +43,7 @@ class JacobianFactor;
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/**
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/**
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* A conditional Gaussian functions as the node in a Bayes network
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* A conditional Gaussian functions as the node in a Bayes network
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* It has a set of parents y,z, etc. and implements a probability density on x.
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* It has a set of parents y,z, etc. and implements a probability density on x.
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* The negative log-probability is given by || Rx - (d - Sy - Tz - ...)||^2
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* The negative log-probability is given by \f$ |Rx - (d - Sy - Tz - ...)|^2 \f$
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*/
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*/
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class GaussianConditional : public IndexConditional {
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class GaussianConditional : public IndexConditional {
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@ -65,7 +65,7 @@ public:
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protected:
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protected:
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/** Store the conditional as one big upper-triangular wide matrix, arranged
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/** Store the conditional as one big upper-triangular wide matrix, arranged
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* as [ R S1 S2 ... d ]. Access these blocks using a VerticalBlockView.
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* as \f$ [ R S1 S2 ... d ] \f$. Access these blocks using a VerticalBlockView.
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*
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*
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* WARNING!!! When using with LDL, R is the permuted upper triangular matrix.
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* WARNING!!! When using with LDL, R is the permuted upper triangular matrix.
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* Its columns/rows do not correspond to the correct components of the variables.
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* Its columns/rows do not correspond to the correct components of the variables.
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@ -110,7 +110,7 @@ public:
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/**
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/**
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* constructor with number of arbitrary parents (only used in unit tests,
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* constructor with number of arbitrary parents (only used in unit tests,
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* std::list is not efficient)
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* std::list is not efficient)
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* |Rx+sum(Ai*xi)-d|
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* \f$ |Rx+sum(Ai*xi)-d| \f$
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*/
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*/
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GaussianConditional(Index key, const Vector& d,
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GaussianConditional(Index key, const Vector& d,
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const Matrix& R, const std::list<std::pair<Index, Matrix> >& parents, const Vector& sigmas);
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const Matrix& R, const std::list<std::pair<Index, Matrix> >& parents, const Vector& sigmas);
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@ -199,7 +199,7 @@ public:
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* assuming that parents have been solved already.
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* assuming that parents have been solved already.
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*
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*
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* @param x values structure with solved parents, and the RHS for this conditional
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* @param x values structure with solved parents, and the RHS for this conditional
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* @return solution x = R \ (d - Sy - Tz - ...) for each frontal variable
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* @return solution \f$ x = R \ (d - Sy - Tz - ...) \f$ for each frontal variable
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*/
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*/
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void solveInPlace(VectorValues& x) const;
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void solveInPlace(VectorValues& x) const;
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