ReBEL ChangeLog***************This is the main ReBEL Changelog file documenting the ongoing evolution and debugging process of the ReBEL Toolkit. Below the changes from onemajor or minor release to the next is outlined. The numbers in bracketsafter each release version number is the number of subsequent 'bugfix' releases which has been made. So "Version 0.2 (1,2)" implies 2 bugfix versions has been released since the release of ReBEL-0.2, namelyReBEL-0.2.1 and ReBEL-0.2.2. The details of each bugfix release is documented in more detail in the Bugfixes file which can be found the root ReBEL directory.		Version 0.2 (1,2,3,4,5,6)===========* Added new inference algorithms:  - Gaussian Sum Particle Filter   (gspf)  - Gaussian Mixture Sigma-Point Particle Filter (gmsppf)         <still in beta state>  These hybrid particle filters try to improve on the generic bootstrap   particle filter. The GSPF (developed by J. Kotecha & P. Djuric) uses   a GMM representation of the posterior state distribution, efectively   smoothing out the particle based representation. This filter requires   the noise sources to be modeled as GMMs themselves.   The GMSPPF is a hybrid extension of the Sigma-Point Particle Filter   (sppf) that uses a SPKF-filterbank propagated GMM proposal distribution   to sample the particles from. The actual posterior state distribution   is also modeled with a GMM (like in the GSPF), but the difference is   that this GMM is fitted to the posterior weighted particle set (as   generated by the importance sampling step) by means of an EM algorithm.  This eliminate the need for a variance increasing final resampling stage.  This filter has equal (or better) performance compared to the SPPF, but   has a significantly lower computational cost. See code for more details.	* Added a new noise source type to the 'gennoiseds' function. This is  a Gaussian mixture model noise source (type='gmm'). See gennoiseds.m  for more detail.	* Added a new data structure (GMM) and functions to the core module of   ReBEL to support Gaussian mixture models (GMMs). These functions are:    - gmmfit : Fit/train a GMM to data using the EM algorithm.    - gmminitialize : Initiliaze a GMM (used internally by gmmfit)    - gmmsample : Sample efficiently from a GMM    - gmmprobability : calculate all probabilities of a GMM and a related 	               dataset.   See the function definitions for more detail.	* The Netlab toolkit (neural network software for Matlab) is now bundled   with ReBEL. This is not a needed/crucial component of ReBEL, but   complements it nicely when certain neural network structures are needed  within user defined models, etc. For more detail on Netlab, see  http://www.ncrg.aston.ac.uk/netlab/ (Thanks to Ian Nabney and   Christopher Bishop for developing such a usefull resource). * Added more examples:  - Dual Estimation : Added a speech enhancement demo based on dual SPKF     estimation. A speech fragment (phoneme), corrupted by additive    colored Gaussian noise is cleaned up (filtered) through the use of a    dual SPKF estimator. This example demonstrates how colored noise     is implemented withion the ReBEL framework as well as how dual     estimation is done.* All particle filters : Changed the definition for all particle filters  of the '.resampleThreshold' field in the InferenceDS data structure.  It used to be an absolute number of particles, the threshold size  of the effective particle set. This (the threshold) has now been    changed to a relative ratio of resampleThreshold = N_efective/N_total.	* Changes (actually additions) to the parameter estimation  meta system blocks in 'geninfds.m':  - Changed the paramFunSelect option of 'both' to 'both-p' which    indicates the use of a parallel combination of FFUN and HFUN in    the parameter estimation observation function, i.e.                    observ = |FFUN(X)|                   |HFUN(X)|  - Changed the meaning and implementation of the paramFunSelect    option 'both'. This option (the default) now implies a serial    concatenation of the original system state transition and state    observation functions (FFUN and HFUN) to form the observation    function for parameter estimation. The following is now implied    for the paramFunSelect='both' option          observ = HFUN(FFUN(X)) * Made changes to the NoiseDS data structure:  - Changed '.ns_subtype' field to '.cov_type' (covariance type)    Note : The '.subtype' field of the argument data structure used as    input to the 'gennoiseds' function has also changed to '.cov_type'.  - Changed covariance field name in all 'NoiseDS' data structures from    '.P' and '.S' to '.cov'. This might seem ambiguous, but the type of    covariance (full, sqrt, diag, etc.) is already clearly evident    from the '.cov_type' field.  - Removed the '.sqrt_flag' and '.diag_flag' fields. These where    superfluous and did not serve the orginaly planned beneficial    purpose.* Bug fixes : Numerous bugs/typos have been fixed. Some of the more    serious ones were:    - geninfds::linearize_generic : 	 o Interface to function requires a varargout list which was 	   not honored. This is now fixed.	 o Internal indexing error.        Version 0.1 (1)===========* Initial Alpha release