, ▁which ▁makes ▁it ▁general ▁enough ▁to ▁support ▁different ▁data ▁represented ▁by ▁the ▁data ▁graph . ▁Thus , ▁it ▁is ▁possible ▁to ▁use ▁a ▁given ▁data ▁set ▁to ▁learn ▁the ▁structure ▁and ▁conditional ▁probability ▁distributions ▁of ▁the ▁model ▁graph ▁and ▁then ▁generate ▁the ▁inference ▁graph ▁from ▁the ▁model ▁graph ▁applied ▁to ▁a ▁data ▁graph ▁that ▁represents ▁another ▁set ▁of ▁data . ▁In ference ▁graph : ▁It ▁corresponds ▁to ▁that ▁graph ▁generated ▁by ▁both ▁data ▁graph ▁and ▁model ▁graph ▁in ▁a ▁process ▁called ▁roll ▁out . ▁In ference ▁graphs ▁are ▁probably ▁larger ▁than ▁data ▁graphs ▁and ▁model ▁graphs ▁due ▁each ▁of ▁the ▁attributes ▁for ▁each ▁of ▁the ▁objects ▁is ▁an ▁instance ▁on ▁the ▁inference ▁graph ▁with ▁the ▁characteristics ▁of ▁that ▁corresponding ▁attribute ▁from ▁model ▁graph . ▁▁ ▁In ▁summary , ▁the ▁data ▁graph ▁gu ides ▁how ▁the ▁model ▁graph ▁will ▁be ▁rolled ▁out ▁to ▁generate ▁the ▁inference ▁graph . ▁ ▁R DN ▁Learning ▁ ▁The ▁learning ▁method ▁for ▁a ▁R DN ▁is ▁similar ▁to ▁that ▁method ▁used ▁by ▁D N s , ▁that ▁is , ▁all ▁conditional ▁probability ▁distributions ▁can ▁be ▁learned ▁for ▁each ▁of ▁the ▁variables ▁independently . ▁However , ▁only ▁conditional ▁rel ational ▁learn ers ▁can ▁be ▁used ▁during ▁parameters ▁estimation ▁process ▁for ▁R DN s . ▁Therefore , ▁that ▁learn ers ▁used ▁by ▁D N s , ▁like ▁decision ▁trees ▁or ▁log istic ▁regression , ▁don ' t ▁work ▁for ▁R DN s . ▁Ne ville , ▁J ., ▁& ▁J ensen , ▁D . ▁( 2 0 0 7 )