[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-217203-en":3,"doc-seo-217203-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},217203,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",6,"Technology","Markov Random Field Structure Estimator - Version 0.4.2 - Package mrfse","Markov random field structure estimator package mrfse provides three algorithms to estimate Markovian neighborhoods from discrete samples. It includes an exact version and a simulated annealing variant of a penalized maximum conditional likelihood method, aligned with a Bayesian Information Criterion-like objective, plus a greedy approach based on referenced learning of Ising models on arbitrary graphs. The functions return estimated neighborhoods for each vertex or adjacency matrices, controlled by alphabet size, a hyperparameter tau, and a max_degree constraint. Licensed under GPL.","Package ‘mrfse’  \nMay 9, 2026  \nTitle Markov Random Field Structure Estimator Version 0.4.2  \nDate 2024-10-21  \nDescription Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are de  \nscribed in Frondana (2016) \u003Cdoi:10 . 11606/T.45.2018.tde-02022018-  \n151123>.The third one is a greedy algorithm, de  \nscribed in Bresler (2015) \u003Cdoi:10 . 1145/2746539 .2746631) .  \nLicense GPL (>= 3)  \nMaintainer Rodrigo Carvalho \u003C[rodrigorsdc@gmail.com](rodrigorsdc@gmail.com)> LinkingTo Rcpp  \nImports Rcpp Depends gtools, Rfast NeedsCompilation yes  \nAuthor Rodrigo Carvalho [aut, cre],  \nFlorencia Leonardi [rev, ths] Repository CRAN  \nDate/Publication 2024-10-21 17:40:01 UTC  \nContents  \n[mrfse.ci ...........................................](mrfse.ci ........................................... 2)[ 2](mrfse.ci ........................................... 2)  \n[mrfse.ci.con .........................................](mrfse.ci.con ......................................... 3)[ 3](mrfse.ci.con ......................................... 3)  \n[mrfse.ci.ncon ........................................](mrfse.ci.ncon ........................................ 4)[ 4](mrfse.ci.ncon ........................................ 4)  \n[mrfse.create.sampler ....................................](mrfse.create.sampler .................................... 5)[ 5](mrfse.create.sampler .................................... 5)  \n[mrfse.exact .........................................](mrfse.exact ......................................... 6)[ 6](mrfse.exact ......................................... 6)  \n[mrfse.exact.con .......................................](mrfse.exact.con ....................................... 7)[ 7](mrfse.exact.con ....................................... 7)  \n[mrfse.exact.ncon ......................................](mrfse.exact.ncon ...................................... 8)[ 8](mrfse.exact.ncon ...................................... 8)  \n[mrfse.sa ...........................................](mrfse.sa ........................................... 9)[ 9](mrfse.sa ........................................... 9)  \n[mrfse.sa.con .........................................](mrfse.sa.con ......................................... 10)[ 10](mrfse.sa.con ......................................... 10)  \n[mrfse.sa.ncon ........................................](mrfse.sa.ncon ........................................ 11)[ 11](mrfse.sa.ncon ........................................ 11)  \n[mrfse.sample ........................................](mrfse.sample ........................................ 12)[ 12](mrfse.sample ........................................ 12)  \n[2 mrfse.ci](2 mrfse.ci)  \nIndex 13  \n\n| [mrfse.ci](mrfse.ci) | Bresler’s non-binary Markov random field structure estimator |\n| --- | --- |\n\nDescription  \nA greedy algorithm to estimate Markovian neighborhoods.  \nUsage  \n[mrfse.ci](mrfse.ci) (a_size, sample, tau, max_degree=ncol(sample)-1)  \nArguments  \na_size Size of the alphabet.  \nsample A integer-valued matrix. Each value must belong range 0 and a_size-1. Ma  \ntrix has dimension n x V, where n is number of samples and V is number of nodes.  \ntau A hyperparameter. See references.  \nmax_degree The maximum length of a candidate Markovian neighborhood. Must be nonnegative and less than ncol(sample) .  \nValue  \nA list filled with estimated Markov neighborhood for each graph vertex  \nAuthor(s)  \nRodrigo Carvalho  \nReferences  \nGuy Bresler. 2015. Efficiently Learning Ising Models on Arbitrary Graphs. In Proceedings of the forty-seventh annual ACM symposium on Theory of Computing (STOC’15) . Association for Computing Machinery, New York, NY, USA, 771–782. DOI:[https://doi.org/10.1145/2746539.2746631](https://doi.org/10.1145/2746539.2746631)  \nExamples  \nlibrary(mrfse)  \na_size = c(0, 1","cbCaigs2zZfrj5vS","https://ap.wps.com/l/cbCaigs2zZfrj5vS","pdf",94952,1,13,"English","en",105,"# mrfse.ci - Bresler’s non-binary Markov random field structure estimator\n## Usage\n## Arguments\n## Value\n## References\n# mrfse.ci.con - Conservative approach for Bresler’s non-binary estimator\n## Usage\n## Arguments\n## Value\n## References\n# mrfse.ci.ncon - Non-conservative approach for Bresler’s non-binary estimator\n## Usage\n## Arguments\n## Value\n## References","[{\"question\":\"What problem does mrfse address?\",\"answer\":\"mrfse estimates Markovian neighborhoods and Markov random field structure from integer-valued sample matrices.\"},{\"question\":\"Which algorithms are provided by the package?\",\"answer\":\"It provides a greedy estimator plus exact and simulated annealing variants of a penalized maximum conditional likelihood method similar to a BIC-style criterion.\"},{\"question\":\"How does max_degree affect the results?\",\"answer\":\"max_degree sets the maximum length of candidate Markovian neighborhoods and must be nonnegative and less than the number of columns in the sample matrix minus one.\"}]","Markov Random Field Structure Estimator - 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