X-Git-Url: http://git.tuebingen.mpg.de/?p=libdai.git;a=blobdiff_plain;f=include%2Fdai%2Falldai.h;h=c0010ca1dfcf0f7cc43d65e67ff2ea8ea25c76df;hp=3bf9b2dfae127f2cd40246b64cc27750edbbeabf;hb=87c6827445f8fd67801efb6e818771e16229313b;hpb=83f2623b5af4926236d6aeae2a98d8e068d4424f

diff --git a/include/dai/alldai.h b/include/dai/alldai.h
index 3bf9b2d..c0010ca 100644
--- a/include/dai/alldai.h
+++ b/include/dai/alldai.h
@@ -22,6 +22,7 @@
 
 /// \file
 /// \brief Main libDAI header file
+/// \todo Improve documentation
 
 
 #ifndef __defined_libdai_alldai_h
@@ -32,6 +33,8 @@
 #include <dai/daialg.h>
 #include <dai/properties.h>
 #include <dai/exactinf.h>
+#include <dai/evidence.h>
+#include <dai/emalg.h>
 #ifdef DAI_WITH_BP
     #include <dai/bp.h>
 #endif
@@ -53,6 +56,9 @@
 #ifdef DAI_WITH_MR
     #include <dai/mr.h>
 #endif
+#ifdef DAI_WITH_GIBBS
+    #include <dai/gibbs.h>
+#endif
 
 
 /// Namespace for libDAI
@@ -91,6 +97,9 @@ static const char* DAINames[] = {
 #endif
 #ifdef DAI_WITH_MR
     MR::Name,
+#endif
+#ifdef DAI_WITH_GIBBS
+    Gibbs::Name,
 #endif
     ""
 };
@@ -99,95 +108,4 @@ static const char* DAINames[] = {
 } // end of namespace dai
 
 
-/** \mainpage libDAI reference manual
- *  \author Joris Mooij
- *  \version 0.2.2
- *  \date 30 September 2008
- *
- *  \section about About libDAI
- *  libDAI is a free/open source C++ library (licensed under GPL) that provides
- *  implementations of various (approximate) inference methods for discrete 
- *  graphical models. libDAI supports arbitrary factor graphs with discrete 
- *  variables; this includes discrete Markov Random Fields and Bayesian 
- *  Networks.
- *
- *  The library is targeted at researchers; to be able to use the library, a 
- *  good understanding of graphical models is needed. 
- *
- *  \section limitations Limitations
- *  libDAI is not intended to be a complete package for approximate inference. 
- *  Instead, it should be considered as an "inference engine", providing 
- *  various inference methods. In particular, it contains no GUI, currently 
- *  only supports its own file format for input and output (although support 
- *  for standard file formats may be added later), and provides very limited
- *  visualization functionalities.
- *
- *  \section features Features
- *  Currently, libDAI supports the following (approximate) inference methods:
- *  - Exact inference by brute force enumeration;
- *  - Exact inference by junction-tree methods;
- *  - Mean Field;
- *  - Loopy Belief Propagation [\ref KFL01];
- *  - Tree Expectation Propagation [\ref MiQ04];
- *  - Generalized Belief Propagation [\ref YFW05];
- *  - Double-loop GBP [\ref HAK03];
- *  - Various variants of Loop Corrected Belief Propagation
- *    [\ref MoK07, \ref MoR05].
- *
- *  \section language Why C++?
- *  Because libDAI is implemented in C++, it is very fast compared with
- *  implementations in MatLab (a factor 1000 faster is not uncommon).
- *  libDAI does provide a MatLab interface for easy integration with MatLab. 
- *
- */
-
-
-/** \page Bibliography
- *  \section Bibliograpy
- *  \anchor KFL01 \ref KFL01
- *  F. R. Kschischang and B. J. Frey and H.-A. Loeliger (2001):
- *  "Factor Graphs and the Sum-Product Algorithm",
- *  <em>IEEE Transactions on Information Theory</em> 47(2):498-519.
- *  http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=910572
- *
- *  \anchor MiQ04 \ref MiQ04
- *  T. Minka and Y. Qi (2004):
- *  "Tree-structured Approximations by Expectation Propagation",
- *  <em>Advances in Neural Information Processing Systems</em> (NIPS) 16.
- *  http://books.nips.cc/papers/files/nips16/NIPS2003_AA25.pdf
- *
- *  \anchor MoR05 \ref MoR05
- *  A. Montanari and T. Rizzo (2005):
- *  "How to Compute Loop Corrections to the Bethe Approximation",
- *  <em>Journal of Statistical Mechanics: Theory and Experiment</em>
- *  2005(10)-P10011.
- *  http://stacks.iop.org/1742-5468/2005/P10011
- *
- *  \anchor YFW05 \ref YFW05
- *  J. S. Yedidia and W. T. Freeman and Y. Weiss (2005):
- *  "Constructing Free-Energy Approximations and Generalized Belief Propagation Algorithms",
- *  <em>IEEE Transactions on Information Theory</em>
- *  51(7):2282-2312.
- *  http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1459044
- *
- *  \anchor HAK03 \ref HAK03
- *  T. Heskes and C. A. Albers and H. J. Kappen (2003):
- *  "Approximate Inference and Constrained Optimization",
- *  <em>Proceedings of the 19th Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)</em> pp. 313-320.
- *  http://www.snn.ru.nl/reports/Heskes.uai2003.ps.gz
- *
- *  \anchor MoK07 \ref MoK07
- *  J. M. Mooij and H. J. Kappen (2007):
- *  "Loop Corrections for Approximate Inference on Factor Graphs",
- *  <em>Journal of Machine Learning Research</em> 8:1113-1143.
- *  http://www.jmlr.org/papers/volume8/mooij07a/mooij07a.pdf
- *
- *  \anchor MoK07b \ref MoK07b
- *  J. M. Mooij and H. J. Kappen (2007):
- *  "Sufficient Conditions for Convergence of the Sum-Product Algorithm",
- *  <em>IEEE Transactions on Information Theory</em> 53(12):4422-4437.
- *  http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4385778
- */
-
-
 #endif