IS = { zkontrolovano 25 Jan 2010 },
  UPDATE  = { 2009-07-27 },
   author =      {Werner, Tom{\'a}{\v s}},
   title =       {Revisiting the Decomposition Approach to Inference in
                  Exponential Families and Graphical Models},
   institution = {Center for Machine Perception, 
                  K13133 FEE Czech Technical University},
   address =     {Prague, Czech Republic},
   year =        {2009},
   month =       {May},
   type =        {Research Report},
   number =      {CTU--CMP--2009--06},
   issn =        {1213-2365},
   pages =       {15},
   figures =     {7},
   authorship =  {100},
   psurl =       {[Werner-TR-2009-06.pdf]},
   project =     {ICT-215078 DIPLECS, MSM6840770038},
   annote = {The approach to upper-bounding the (log-)partition
     function and the modes of a probability distribution from a
     general exponential family is revisited. This approach is based
     on decomposition of the original problem into tractable
     subproblems. It was proposed by Wainwright et al. for undirected
     graphical models (MRFs) and tree-structured subproblems. We
     generalize it to general subproblems and general exponential
   keywords =    {undirected graphical model, Markov random field, 
                  exponential family, mean polytope, 
                  problem decomposition, inference,
                  log-linear model},