▁ ▁present ▁some ▁experiments ▁results ▁comparing ▁R DN s ▁when ▁learning ▁with ▁Rel ational ▁Bay esian ▁Class ifiers ▁and ▁R DN s ▁when ▁learning ▁with ▁Rel ational ▁Pro b ability ▁Tre es . ▁N atar aj an ▁et ▁al . ▁( 2 0 1 2 ) ▁ ▁use ▁a ▁serie ▁of ▁regression ▁models ▁to ▁represent ▁conditional ▁distributions . ▁ ▁This ▁learning ▁method ▁makes ▁the ▁R DN ▁a ▁model ▁with ▁an ▁efficient ▁learning ▁time . ▁However , ▁this ▁method ▁also ▁makes ▁R DN s ▁sus cept ible ▁to ▁some ▁struct ural ▁or ▁numerical ▁incons ist ences . ▁If ▁the ▁conditional ▁probability ▁distribution ▁estimation ▁method ▁uses ▁feature ▁selection , ▁it ' s ▁possible ▁that ▁a ▁given ▁variable ▁finds ▁a ▁dependency ▁among ▁it ▁and ▁another ▁variable ▁while ▁the ▁latter ▁doesn ' t ▁find ▁this ▁dependency . ▁In ▁this ▁case , ▁the ▁R DN ▁is ▁struct ur ally ▁incons istent . ▁In ▁addition , ▁if ▁the ▁joint ▁distribution ▁doesn ' t ▁sum ▁one ▁due ▁to ▁approxim ations ▁caused ▁by ▁the ▁independent ▁learning , ▁then ▁we ▁say ▁that ▁there ▁is ▁a ▁numerical ▁incons istence . ▁Fort un ately , ▁such ▁incons ist ences ▁can ▁be ▁by pass ed ▁during ▁the ▁inference ▁step , ▁as ▁we ▁will ▁see ▁soon ▁in ▁the ▁R DN ▁inference ▁section . ▁ ▁R DN ▁In ference ▁ ▁R DN ▁inference ▁begins ▁with ▁the ▁creation ▁of ▁inference ▁graph ▁through ▁a ▁process ▁called ▁roll ▁out . ▁In ▁this ▁process , ▁the ▁model ▁graph ▁is ▁rolled ▁out ▁over ▁the ▁data ▁graph ▁to ▁form ▁the ▁inference ▁graph .