/// \file
/// \brief Main libDAI header file
/// \todo Improve documentation
-/// \todo Improve documentation of examples/example
#ifndef __defined_libdai_alldai_h
#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
#ifdef DAI_WITH_MR
#include <dai/mr.h>
#endif
+#ifdef DAI_WITH_GIBBS
+ #include <dai/gibbs.h>
+#endif
/// Namespace for libDAI
#endif
#ifdef DAI_WITH_MR
MR::Name,
+#endif
+#ifdef DAI_WITH_GIBBS
+ Gibbs::Name,
#endif
""
};
} // end of namespace dai
-/** \mainpage libDAI reference manual
- * \author Joris Mooij
- * \version git HEAD
- * \date October 8, 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.
- *
- * \section quickstart Quick start
- * An example program illustrating basic usage of libDAI is given in examples/example.cpp.
- */
-
-/// \example example.cpp
-
-/** \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
projects / libdai.git / blobdiff