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The approach emphasizes extracting conditional independence information, using information-theoretic scoring rules such as AIC, BIC, and MDL, combined with Bayesian scoring functions and exact or heuristic structure search. Empirical modeling is used to discover associations in complex, high-dimensional datasets. Future directions include broader distribution classes and boosted information-theoretic scores, alongside adjustments for confounders via an adjusted DAG and consideration of issues such as confounding, dependence structure, and multicollinearity.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/information-theoretic-scoring-rules-to-learn-additive-bayesian-network-applied-to-epidemiology-paper/290094/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/information-theoretic-scoring-rules-to-learn-additive-bayesian-network-applied-to-epidemiology-paper/290094.png","ImageObject",300,407,{"name":92,"@type":93},"\tWilliam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the additive Bayesian network (ABN) approach address in epidemiology?","Question",{"text":112,"@type":113},"ABN is designed to extract conditional independence information from observational datasets, helping model complex and messy data and discover associations in high-dimensional settings.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which scoring methods are implemented and used for Bayesian network learning?",{"text":117,"@type":113},"The document lists implemented information-theory scores including AIC, BIC, and MDL, together with a Bayesian scoring function for structure learning.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the method handle confounding in the learned network?",{"text":121,"@type":113},"It discusses the possibility of computing an adjusted DAG using classical epidemiological confounders, aiming to account for confounding effects in the structure.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290094,1789640411,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":81,"preview_page_count":4},5909887254083,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Information-Theoretic Scoring Rules to Learn  \nAdditive Bayesian Network Applied to Epidemiology  \nGilles Kratzer 1 , Reinhard Furrer 1,2  \n1 Department of Mathematics, 2 Department of Computational Science; University of Zurich ( Switzerland ) Contact: [gilles.kratzer@math.uzh.ch](gilles.kratzer@math.uzh.ch)  \nMotivation  \n• ABN 1 methodology extends the classical generalized linear model (GLM) framework to multiple dependent variables  \n• The key perspective of ABN is to extract the conditional independence information from an observational dataset  \n• ABN is a suitable methodology to mastermind complex and messy data in an exploratory analysis  \nSummary  \n• ABN is a mixture between machine learning and statistical approach  \n• abn is distributed as an R package [https://CRAN.R-project.org/package=abn](https://CRAN.R-project.org/package=abn)  \n• Several implemented information theory scores: AIC, BIC, MDL  \n• Bayesian scoring function  \n• Exact and Heuristic search algorithm  \nResults  \n• Perform structure discovery  \n• ABN modelling empirically identifies associations in complex and high dimensional data as a machine learning technique  \nFuture Work  \n• Implementation of wider classes of  \ndistributions  \n• Implementation of boosted information theoretic scores  \nWhy thinking in terms of system?  \n• System epidemiology = contributions at different levels  \n• Confounding factors  \n• Complex dependance structure  \n• Multicollinearity  \nHow to learn Bayesian Networks from data using search and score method 2 ?  \nWhy Information-theoretic scores?  \nData separation and sparsity  \nNo optimal solution for Bayesian regression in the ABN context  \nMultinomial Distribution  \nActually no implementation of multinomial Bayesian estimation with suitable prior  \nEffective computation (fast and reliable)  \nIterative reweighting least square method is fast and highly reliable  \nAdjustment for confounders  \nPossibility to compute an adjusted DAG with classical epidemiological confounders  \n\n| Illustrative example 3\u003Cbr>Subset of the 1987 National Indonesia Contraceptive Prevalence Survey. Selection criteria: no pregnant and married womans.\u003Cbr>10 variables, n = 1473 observations (no missing data)\u003Cbr>• Wife‘s age (normal)\u003Cbr>• Husband education (multinomial, 4 levels)\u003Cbr>• Number of children ( Poisson )\u003Cbr>• Wife‘s religion (binomial)\u003Cbr>• Wife is currentely working (binomial)\u003Cbr>• Husband‘s occupation (multinomial, 4 levels)\u003Cbr>• Standard-of-living index (multinomial, 4 levels)\u003Cbr>• Media exposure (binomial)\u003Cbr>• Contraceptive method used (multinomial, 3 levels) |  |\n| --- | --- |\n| \u003Cbr>Wife Age\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>Wife Working\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>Children\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>| Node distribution\u003Cbr> Normal  Poisson  Binomial  Multinomial\u003Cbr>Structure contribution\u003Cbr> Wife related variables  Husband related variables  Jointely related variables |\n\nReferences  \n1. Lewis, F. I. et al. \"Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data“, Preventive Veterinary Medicine 100.2 (2011): 109-115.  \n2. Koivisto, M. et al..“Exact Bayesian structure discovery in Bayesian networks“. Journal of Machine Learning Research, 549-573. (2004)  \n3. Lim, T.-S. et al. \"A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms“, Machine Learning (1999) .","cbCaiqYo6vhQZr7N","https://ap.wps.com/l/cbCaiqYo6vhQZr7N","pdf",856981,"English","# Motivation\n# Summary\n# Results\n# Future Work\n# Why thinking in terms of system?\n# How to learn Bayesian Networks from data using search and score method\n# Why Information-theoretic scores?\n## Data separation and sparsity\n## Multinomial distribution\n## Effective computation\n## Adjustment for confounders\n# Illustrative example","[{\"question\":\"What problem does the additive Bayesian network (ABN) approach address in epidemiology?\",\"answer\":\"ABN is designed to extract conditional independence information from observational datasets, helping model complex and messy data and discover associations in high-dimensional settings.\"},{\"question\":\"Which scoring methods are implemented and used for Bayesian network learning?\",\"answer\":\"The document lists implemented information-theory scores including AIC, BIC, and MDL, together with a Bayesian scoring function for structure learning.\"},{\"question\":\"How does the method handle confounding in the learned network?\",\"answer\":\"It discusses the possibility of computing an adjusted DAG using classical epidemiological confounders, aiming to account for confounding effects in the structure.\"}]","Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology - Paper | PDF"]