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\section{Class List}
Here are the classes, structs, unions and interfaces with brief descriptions:\begin{CompactList}
\item\contentsline{section}{\hyperlink{classbdm_1_1ARX}{bdm::ARX} (Linear Autoregressive model with Gaussian noise )}{\pageref{classbdm_1_1ARX}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1ArxDS}{bdm::ArxDS} (Generator of \hyperlink{classbdm_1_1ARX}{ARX} data )}{\pageref{classbdm_1_1ArxDS}}{}
\item\contentsline{section}{\hyperlink{classAssertXercesIsAlive}{AssertXercesIsAlive} (Class initializing Xerces library )}{\pageref{classAssertXercesIsAlive}}{}
\item\contentsline{section}{\hyperlink{classAttribute}{Attribute} (Abstract class declaring general properties of a frame for data binding )}{\pageref{classAttribute}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1bdmroot}{bdm::bdmroot} (Root class of BDM objects )}{\pageref{classbdm_1_1bdmroot}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1bilinfn}{bdm::bilinfn} (Class representing function $f(x,u) = Ax+Bu$ )}{\pageref{classbdm_1_1bilinfn}}{}
\item\contentsline{section}{\hyperlink{classBindingFrame}{BindingFrame} (Abstract class declaring general properties of a frame for data binding )}{\pageref{classBindingFrame}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1BM}{bdm::BM} (Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities )}{\pageref{classbdm_1_1BM}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1BMcond}{bdm::BMcond} (Conditional Bayesian Filter )}{\pageref{classbdm_1_1BMcond}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1BMEF}{bdm::BMEF} (Estimator for Exponential family )}{\pageref{classbdm_1_1BMEF}}{}
\item\contentsline{section}{\hyperlink{classchmat}{chmat} (Symmetric matrix stored in square root decomposition using upper cholesky )}{\pageref{classchmat}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1compositepdf}{bdm::compositepdf} (Abstract composition of pdfs, will be used for specific classes this abstract class is common to \hyperlink{classbdm_1_1epdf}{epdf} and \hyperlink{classbdm_1_1mpdf}{mpdf} )}{\pageref{classbdm_1_1compositepdf}}{}
\item\contentsline{section}{\hyperlink{classCompoundUserInfo}{CompoundUserInfo$<$ T $>$} (The main userinfo template class. You should derive this class whenever you need a new userinfo of a class which is compound from smaller elements (all having its own userinfo class prepared) )}{\pageref{classCompoundUserInfo}}{}
\item\contentsline{section}{\hyperlink{classCompoundUserInfo_1_1BindedElement}{CompoundUserInfo$<$ T $>$::BindedElement$<$ U $>$} (Templated class binding inner element with its XML tag and automating data transfers in both directions )}{\pageref{classCompoundUserInfo_1_1BindedElement}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1constfn}{bdm::constfn} (Class representing function $f(x) = a$, here {\tt rv} is empty )}{\pageref{classbdm_1_1constfn}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1datalink__e2e}{bdm::datalink\_\-e2e} (DataLink is a connection between two data vectors Up and Down )}{\pageref{classbdm_1_1datalink__e2e}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1datalink__m2e}{bdm::datalink\_\-m2e} (Data link between )}{\pageref{classbdm_1_1datalink__m2e}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1datalink__m2m}{bdm::datalink\_\-m2m} }{\pageref{classbdm_1_1datalink__m2m}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1diffbifn}{bdm::diffbifn} (Class representing a differentiable function of two variables $f(x,u)$ )}{\pageref{classbdm_1_1diffbifn}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1dirfilelog}{bdm::dirfilelog} (Logging into dirfile with buffer in memory )}{\pageref{classbdm_1_1dirfilelog}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1DS}{bdm::DS} (Abstract class for discrete-time sources of data )}{\pageref{classbdm_1_1DS}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1eDirich}{bdm::eDirich} (Dirichlet posterior density )}{\pageref{classbdm_1_1eDirich}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1eEF}{bdm::eEF} (General conjugate exponential family posterior density )}{\pageref{classbdm_1_1eEF}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1eEmp}{bdm::eEmp} (Weighted empirical density )}{\pageref{classbdm_1_1eEmp}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1egamma}{bdm::egamma} (Gamma posterior density )}{\pageref{classbdm_1_1egamma}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1egiw}{bdm::egiw} (Gauss-inverse-Wishart density stored in LD form )}{\pageref{classbdm_1_1egiw}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1eigamma}{bdm::eigamma} (Inverse-Gamma posterior density )}{\pageref{classbdm_1_1eigamma}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1EKF}{bdm::EKF$<$ sq\_\-T $>$} (Extended \hyperlink{classbdm_1_1Kalman}{Kalman} Filter )}{\pageref{classbdm_1_1EKF}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1EKFCh}{bdm::EKFCh} (Extended \hyperlink{classbdm_1_1Kalman}{Kalman} Filter in Square root )}{\pageref{classbdm_1_1EKFCh}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1EKFCh__cond}{bdm::EKFCh\_\-cond} (Extended \hyperlink{classbdm_1_1Kalman}{Kalman} filter with unknown parameters in {\tt IM} )}{\pageref{classbdm_1_1EKFCh__cond}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1EKFCh__unQ}{bdm::EKFCh\_\-unQ} (Extended \hyperlink{classbdm_1_1Kalman}{Kalman} filter in Choleski form with unknown {\tt Q} )}{\pageref{classbdm_1_1EKFCh__unQ}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1EKFful__unQR}{bdm::EKFful\_\-unQR} (Extended \hyperlink{classbdm_1_1Kalman}{Kalman} filter with unknown {\tt Q} and {\tt R} )}{\pageref{classbdm_1_1EKFful__unQR}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1EKFfull}{bdm::EKFfull} (Extended \hyperlink{classbdm_1_1Kalman}{Kalman} Filter in full matrices )}{\pageref{classbdm_1_1EKFfull}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1emix}{bdm::emix} (Mixture of epdfs )}{\pageref{classbdm_1_1emix}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1enorm}{bdm::enorm$<$ sq\_\-T $>$} (Gaussian density with positive definite (decomposed) covariance matrix )}{\pageref{classbdm_1_1enorm}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1epdf}{bdm::epdf} (Probability density function with numerical statistics, e.g. posterior density )}{\pageref{classbdm_1_1epdf}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1eprod}{bdm::eprod} (Product of independent epdfs. For dependent pdfs, use \hyperlink{classbdm_1_1mprod}{mprod} )}{\pageref{classbdm_1_1eprod}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1euni}{bdm::euni} (Uniform distributed density on a rectangular support )}{\pageref{classbdm_1_1euni}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1fnc}{bdm::fnc} (Class representing function $f(x)$ of variable $x$ represented by {\tt rv} )}{\pageref{classbdm_1_1fnc}}{}
\item\contentsline{section}{\hyperlink{classfsqmat}{fsqmat} (Fake \hyperlink{classsqmat}{sqmat}. This class maps \hyperlink{classsqmat}{sqmat} operations to operations on full matrix )}{\pageref{classfsqmat}}{}
\item\contentsline{section}{\hyperlink{classitpp_1_1Gamma__RNG}{itpp::Gamma\_\-RNG} (Gamma distribution )}{\pageref{classitpp_1_1Gamma__RNG}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1Kalman}{bdm::Kalman$<$ sq\_\-T $>$} (\hyperlink{classbdm_1_1Kalman}{Kalman} filter with covariance matrices in square root form )}{\pageref{classbdm_1_1Kalman}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1KalmanCh}{bdm::KalmanCh} (\hyperlink{classbdm_1_1Kalman}{Kalman} filter in square root form )}{\pageref{classbdm_1_1KalmanCh}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1KalmanFull}{bdm::KalmanFull} (Basic \hyperlink{classbdm_1_1Kalman}{Kalman} filter with full matrices (education purpose only)! Will be deleted soon! )}{\pageref{classbdm_1_1KalmanFull}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1KFcondQR}{bdm::KFcondQR} (\hyperlink{classbdm_1_1Kalman}{Kalman} Filter with conditional diagonal matrices R and Q )}{\pageref{classbdm_1_1KFcondQR}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1KFcondR}{bdm::KFcondR} (\hyperlink{classbdm_1_1Kalman}{Kalman} Filter with conditional diagonal matrices R and Q )}{\pageref{classbdm_1_1KFcondR}}{}
\item\contentsline{section}{\hyperlink{classldmat}{ldmat} (Matrix stored in LD form, (commonly known as UD) )}{\pageref{classldmat}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1linfn}{bdm::linfn} (Class representing function $f(x) = Ax+B$ )}{\pageref{classbdm_1_1linfn}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1logger}{bdm::logger} (Class for storing results (and semi-results) of an experiment )}{\pageref{classbdm_1_1logger}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1mEF}{bdm::mEF} (Exponential family model )}{\pageref{classbdm_1_1mEF}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1MemDS}{bdm::MemDS} (Memory storage of off-line data column-wise )}{\pageref{classbdm_1_1MemDS}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1memlog}{bdm::memlog} (Logging into matrices in data format in memory )}{\pageref{classbdm_1_1memlog}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1mepdf}{bdm::mepdf} (Unconditional \hyperlink{classbdm_1_1mpdf}{mpdf}, allows using \hyperlink{classbdm_1_1epdf}{epdf} in the role of \hyperlink{classbdm_1_1mpdf}{mpdf} )}{\pageref{classbdm_1_1mepdf}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1merger}{bdm::merger} (Function for general combination of pdfs )}{\pageref{classbdm_1_1merger}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1mgamma}{bdm::mgamma} (Gamma random walk )}{\pageref{classbdm_1_1mgamma}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1mgamma__fix}{bdm::mgamma\_\-fix} (Gamma random walk around a fixed point )}{\pageref{classbdm_1_1mgamma__fix}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1migamma}{bdm::migamma} (Inverse-Gamma random walk )}{\pageref{classbdm_1_1migamma}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1migamma__fix}{bdm::migamma\_\-fix} (Inverse-Gamma random walk around a fixed point )}{\pageref{classbdm_1_1migamma__fix}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1MixEF}{bdm::MixEF} (Mixture of Exponential Family Densities )}{\pageref{classbdm_1_1MixEF}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1mlnorm}{bdm::mlnorm$<$ sq\_\-T $>$} (Normal distributed linear function with linear function of mean value; )}{\pageref{classbdm_1_1mlnorm}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1mlstudent}{bdm::mlstudent} }{\pageref{classbdm_1_1mlstudent}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1mmix}{bdm::mmix} (Mixture of mpdfs with constant weights, all mpdfs are of equal type )}{\pageref{classbdm_1_1mmix}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1mpdf}{bdm::mpdf} (Conditional probability density, e.g. modeling some dependencies )}{\pageref{classbdm_1_1mpdf}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1MPF}{bdm::MPF$<$ BM\_\-T $>$} (Marginalized Particle filter )}{\pageref{classbdm_1_1MPF}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1mprod}{bdm::mprod} (Chain rule decomposition of \hyperlink{classbdm_1_1epdf}{epdf} )}{\pageref{classbdm_1_1mprod}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1mratio}{bdm::mratio} (Class representing ratio of two densities which arise e.g. by applying the Bayes rule. It represents density in the form: \[ f(rv|rvc) = \frac{f(rv,rvc)}{f(rvc)} \] where $ f(rvc) = \int f(rv,rvc) d\ rv $ )}{\pageref{classbdm_1_1mratio}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1multiBM}{bdm::multiBM} (Estimator for Multinomial density )}{\pageref{classbdm_1_1multiBM}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1PF}{bdm::PF} (Trivial particle filter with proposal density equal to parameter evolution model )}{\pageref{classbdm_1_1PF}}{}
\item\contentsline{section}{\hyperlink{classRootElement}{RootElement} (This class serves to load and/or save DOMElements into/from files stored on a hard-disk )}{\pageref{classRootElement}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1RV}{bdm::RV} (Class representing variables, most often random variables )}{\pageref{classbdm_1_1RV}}{}
\item\contentsline{section}{\hyperlink{classsqmat}{sqmat} (Virtual class for representation of double symmetric matrices in square-root form )}{\pageref{classsqmat}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1str}{bdm::str} (Structure of \hyperlink{classbdm_1_1RV}{RV} (used internally), i.e. expanded RVs )}{\pageref{classbdm_1_1str}}{}
\item\contentsline{section}{\hyperlink{classTypedUserInfo}{TypedUserInfo$<$ T $>$} (TypeUserInfo is still an abstract class, but contrary to the \hyperlink{classUserInfo}{UserInfo} class it is already templated. It serves as a bridge to non-abstract classes CompoundUserInfo$<$T$>$ or ValuedUserInfo$<$T$>$ )}{\pageref{classTypedUserInfo}}{}
\item\contentsline{section}{\hyperlink{classbdm_1_1UIbuilder}{bdm::UIbuilder} (Builds computational object from a \hyperlink{classUserInfo}{UserInfo} structure )}{\pageref{classbdm_1_1UIbuilder}}{}
\item\contentsline{section}{\hyperlink{classUserInfo}{UserInfo} (\hyperlink{classUserInfo}{UserInfo} is an abstract is for internal purposes only. Use CompoundUserInfo$<$T$>$ or ValuedUserInfo$<$T$>$ instead. The raison d'etre of this class is to allow pointers to its templated descendants )}{\pageref{classUserInfo}}{}
\item\contentsline{section}{\hyperlink{classValuedUserInfo}{ValuedUserInfo$<$ T $>$} (The main userinfo template class. It should be derived whenever you need a new userinfo of a class which does not contain any subelements. It is the case of basic classes(or types) like int, string, double, etc )}{\pageref{classValuedUserInfo}}{}
\end{CompactList}