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 <title>References for HGM</title> <!-- Use UTF-8 文字 code-->  <title>References for HGM</title> <!-- Use UTF-8 文字 code-->
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Line 12  the Holonomic Gradient Descent Method  (HGD) </h1>
Line 14  the Holonomic Gradient Descent Method  (HGD) </h1>
   
 <h2> Papers  and Tutorials</h2>  <h2> Papers  and Tutorials</h2>
 <ol>  <ol>
   <li> Nobuki Takayama, Takaharu Yaguchi, Yi Zhang,
   Comparison of Numerical Solvers for Differential Equations for Holonomic Gradient Method in Statistics,
   <a href="https://arxiv.org/abs/2111.10947"> arxiv:2111.10947 </a>
   
   <li> Shuhei Mano, Nobuki Takayama,
   Algebraic algorithm for direct sampling from toric models,
   <a href="https://arxiv.org/abs/2110.14992"> arxiv:2110.14992 </a>
   
   <li> M.Adamer, A.Lorincz, A.L.Sattelberger, B.Sturmfels, Algebraic Analysis of Rotation Data
   <a href="https://arxiv.org/abs/1912.00396"> arxiv: 1912.00396 </a>
   <li>
   Anna-Laura Sattelberger, Bernd Sturmfels,
   D-Modules and Holonomic Functions
   <a href="https://arxiv.org/abs/1910.01395"> arxiv:1910.01395 </a>
   <li>
   N.Takayama, L.Jiu, S.Kuriki, Y.Zhang,
   Computations of the Expected Euler Characteristic for the Largest Eigenvalue of a Real Wishart Matrix,
   <!--
   <a href="https://arxiv.org/abs/1903.10099"> arxiv:1903.10099 </a> -->
   <a href="https://doi.org/10.1016/j.jmva.2020.104642"> jmva </a>
   <li> M.Harkonen, T.Sei, Y.Hirose,
   Holonomic extended least angle regression,
   <a href="https://arxiv.org/abs/1809.08190"> arxiv:1809.08190 </a>
   <li> S.Mano,
   Partitions, Hypergeometric Systems, and Dirichlet Processes in Statistics,
   <a href="https://www.springer.com/jp/book/9784431558866">
   JSS Research Series in Statistics</a>, 2018.
   <li> A.Kume, T.Sei,
   On the exact maximum likelihood inference of Fisher–Bingham distributions using an adjusted holonomic gradient method,
   <a href="https://doi.org/10.1007/s11222-017-9765-3"> doi </a> (2018)
 <li> Yoshihito Tachibana, Yoshiaki Goto, Tamio Koyama, Nobuki Takayama,  <li> Yoshihito Tachibana, Yoshiaki Goto, Tamio Koyama, Nobuki Takayama,
 Holonomic Gradient Method for Two Way Contingency Tables,  Holonomic Gradient Method for Two Way Contingency Tables,
 <a href="https://arxiv.org/abs/1803.04170"> arxiv:1803.04170 </a>  <a href="https://arxiv.org/abs/1803.04170"> arxiv:1803.04170 </a>
Line 51  region
Line 83  region
 with a multivariate normal distribution,  with a multivariate normal distribution,
 <a href="http://arxiv.org/abs/1512.06564">  arxiv:1512.06564 </a>  <a href="http://arxiv.org/abs/1512.06564">  arxiv:1512.06564 </a>
   
   <li> N.Takayama, Holonomic Gradient Method (in Japanese, survey),
   <a href="http://www.math.kobe-u.ac.jp/HOME/taka/2015/hgm-dic.pdf">
   hgm-dic.pdf </a>
   
 <li> N.Takayama, S.Kuriki, A.Takemura,  <li> N.Takayama, S.Kuriki, A.Takemura,
 A-Hpergeometric Distributions and Newton Polytopes,  A-Hpergeometric Distributions and Newton Polytopes,
Line 251  maximal Likehood estimates for the Fisher-Bingham dist
Line 286  maximal Likehood estimates for the Fisher-Bingham dist
 <li> <a href="http://www.math.kobe-u.ac.jp/OpenXM/Math/Fisher-Bingham-2"> d-dimensional Fisher-Bingham System </a>  <li> <a href="http://www.math.kobe-u.ac.jp/OpenXM/Math/Fisher-Bingham-2"> d-dimensional Fisher-Bingham System </a>
 </ol>  </ol>
   
 <pre> $OpenXM: OpenXM/src/hgm/doc/ref-hgm.html,v 1.24 2018/03/19 01:17:46 takayama Exp $ </pre>  <pre> $OpenXM: OpenXM/src/hgm/doc/ref-hgm.html,v 1.32 2020/08/24 23:24:27 takayama Exp $ </pre>
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