Detailed Description

A class for computing Gaussian marginals of variables in a NonlinearFactorGraph.

Public Member Functions
	Marginals ()
	Default constructor only for Cython wrapper.

	Marginals (const NonlinearFactorGraph &graph, const Values &solution, Factorization factorization=CHOLESKY, EliminateableFactorGraph< GaussianFactorGraph >::OptionalOrdering ordering=boost::none)
	Construct a marginals class. More...

void	print (const std::string &str="Marginals: ", const KeyFormatter &keyFormatter=DefaultKeyFormatter) const
	print

GaussianFactor::shared_ptr	marginalFactor (Key variable) const
	Compute the marginal factor of a single variable.

Matrix	marginalInformation (Key variable) const
	Compute the marginal information matrix of a single variable. More...

Matrix	marginalCovariance (Key variable) const
	Compute the marginal covariance of a single variable.

JointMarginal	jointMarginalCovariance (const KeyVector &variables) const
	Compute the joint marginal covariance of several variables.

JointMarginal	jointMarginalInformation (const KeyVector &variables) const
	Compute the joint marginal information of several variables.

VectorValues	optimize () const
	Optimize the bayes tree.

Public Types
enum	Factorization { CHOLESKY, QR }
	The linear factorization mode - either CHOLESKY (faster and suitable for most problems) or QR (slower but more numerically stable for poorly-conditioned problems). More...

Protected Attributes
GaussianFactorGraph	graph_

Values	values_

Factorization	factorization_

GaussianBayesTree	bayesTree_

Member Enumeration Documentation

◆ Factorization

enum gtsam::Marginals::Factorization

The linear factorization mode - either CHOLESKY (faster and suitable for most problems) or QR (slower but more numerically stable for poorly-conditioned problems).

Constructor & Destructor Documentation

◆ Marginals()

gtsam::Marginals::Marginals	(	const NonlinearFactorGraph &	graph,
		const Values &	solution,
		Factorization	factorization = `CHOLESKY`,
		EliminateableFactorGraph< GaussianFactorGraph >::OptionalOrdering	ordering = `boost::none`
	)

Construct a marginals class.

Parameters

graph	The factor graph defining the full joint density on all variables.
solution	The linearization point about which to compute Gaussian marginals (usually the MLE as obtained from a NonlinearOptimizer).
factorization	The linear decomposition mode - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).
ordering	An optional variable ordering for elimination.

Member Function Documentation

◆ marginalInformation()

Matrix gtsam::Marginals::marginalInformation ( Key variable ) const

Compute the marginal information matrix of a single variable.

Use LLt(const Matrix&) or RtR(const Matrix&) to obtain the square-root information matrix.

The documentation for this class was generated from the following files:

/Users/dellaert/git/gtsam/gtsam/nonlinear/Marginals.h
/Users/dellaert/git/gtsam/gtsam/nonlinear/Marginals.cpp