/*! \page app_base bdmtoolbox - List of available basic objects Basic objects of BDM are: - RV decriptor of data vectors and random variables - fnc functions of vector arguments - pdf pdf with non-empty conditioning part - epdf pdf with empty conditioning part - BM Bayesian model - (approximate) calculation of specific type of the Bayes rule, - DS Data Sources - recursive sources of data - logger object storing results of all experiments See, \ref userguide_pdf and \ref userguide_sim for their introduction. \section app_base_rv Working with RV In bdmtoolbox, RV is represented by a matlab structure. It is recommended to use the following function to work with it: - RV, rva=RV('a',1), rvab=RV({'b','c'},[1,2]) create empty RV, RV named "a" of size 1, and vector of RV "b" and "c" of sizes 1 and 2, respectively. - RVtimes(rva,-1) set time (delay) of rva to -1 from current time. - RVjoin([rva,rvb]) joins rva and rvb into a vector \section app_base_fnc Basic functions Predefined functions are: - constfn Class representing function , here rv is empty - linfn Class representing function - mexFnc Matlab extension of a function, calling predefined matlab functions - grid_fnc Function defined by values on a fixed grid and interpolated inbetween them \section app_base_sq Square root decompositions of symetric matrices For information purpose, these matrices are used in epdfs: - chmat Symmetric matrix stored in square root decomposition using upper cholesky - fsqmat Fake sqmat. This class maps sqmat operations to operations on full matrix - ldmat Matrix stored in LD form, (commonly known as UD) \section app_base_epdf Basic epdfs accessible from matlab are: - dirac Dirac delta function distribution - eDirich Dirichlet posterior density - eEF General conjugate exponential family posterior density - eEmp Weighted empirical density - egamma Gamma posterior density - egiw Gauss-inverse-Wishart density stored in LD form - egrid Discrete density defined on a continuous grid - eigamma Inverse-Gamma posterior density - eiWishartCh Inverse Wishart on Choleski decomposition - enorm Gaussian density with positive definite (decomposed) covariance matrix - enorm Gaussian density with positive definite (decomposed) covariance matrix - enorm Gaussian density with positive definite (decomposed) covariance matrix - elognorm LogNormal density - emix Mixture of epdfs - eprod Product of densities, elements may be unconditional, however, the result should be unconditioned pdf. - estudent< ldmat> Student-t density - estudent< ldmat> Student-t density - estudent< ldmat> Student-t density - euni Uniform density on rectangular support - eWishartCh Wishart in choleski decomposition \section app_base_pdf Basic (conditioned) pdfs accessible from matlab are: - mDirich Dirichlet random walk - mexBM BM with functions implemented in matlab - mexEpdf Epdf with functions implemented in matlab - mgamma Gamma random walk - mgamma_fix Gamma random walk around a fixed point - mgdirac Dirac delta pdf with geenral function of the support point - mgnorm< ldmat > Gaussian Pdf with general function for mean value - mgnorm< chmat > Gaussian Pdf with general function for mean value - mgnorm< fsqmat > Gaussian Pdf with general function for mean value - mguni Uniform density with geenral function of mean value - migamma Inverse-Gamma random walk - migamma_ref Inverse-Gamma random walk around a fixed point - mlnorm< ldmat> Normal distribution with linear function with linear function of mean value; - mlnorm< chmat> Normal distribution with linear function with linear function of mean value; - mlnorm< fsqmat> Normal distribution with linear function with linear function of mean value; - mlognorm Log-Normal random walk - mlstudent Student distributed linear function with linear function of mean value; - mmix Mixture of pdfs with constant weights, all pdfs are of equal RV and RVC - mprod Chain rule decomposition of epdf - mratio Class representing ratio of two densities which arise e.g. by applying the Bayes rule. - rwiWishartCh Random Walk on inverse Wishart \section app_base_ds DataSources - MemDS Memory storage of off-line data column-wise - CsvFileDS CSV file data storage The constructor creates Data matrix from the records in a CSV file fname. The orientation can be of two types: 1. BY_COL which is default - the data are stored in columns; one column per time , one row per data item. 2. BY_ROW if the data are stored the classical CSV style. Then each column stores the values for data item, for ex. , one row for each discrete time instant - EpdfDS Simulate data from a static pdf (epdf) - PdfDS Simulate data from conditional density Still having only one density but allowing conditioning on either input or delayed values \section app_base_log Loggers - storage of results - mexlog Logger storing results into an mxArray - dirfilelog Logging into dirfile with buffer in memory - stdlog Simple logger used for debugging All data records are written out to std from where they could be send to file - ITppFileDS Read Data Matrix from an IT file \section app_base_merg Mergers - merger_base Base class for general combination of pdfs on discrete support - merger_mix Merger using importance sampling with mixture proposal density \section app_base_bm Bayesian Models Basic filters: - ARX Linear Autoregressive model with Gaussian noise - ARXfrg ARX with conditioned on forgetting factor - ARXg ARX with Non-linear transformation + Gaussian noise - EKFCh Extended Kalman Filter in Square root - EKFfull Extended Kalman Filter in full matrices - KalmanCh Kalman filter in square root form - KalmanFull Basic Kalman filter with full matrices - MixEF Estimator of Mixtures of Exponential Family Densities - multiBM Estimator for Multinomial density - MultiModel (Switching) Multiple Models. The model runs several BMs in parallel and evaluates thier weights (fittness) - PF Particle filtering: Wrapper for particles - BootstrapParticle Class used in PF - MarginalizedParticle Class used in PF \section app_base_ctrl Controllers (and related classes) - LQG Basic class computing LQG control - LQG_ARX Controller using ARX model for estimation and LQG designer for control - StateFromARX conversion function - StateSpace< ldmat> Basic elements of linear state-space model - StateSpace< chmat> Basic elements of linear state-space model \section app_base_mpdm Multiple Patricipant Decision Makers * ARXAgent ARX agent */