▁Introduction ▁ ▁Rel ational ▁Dep endency ▁Network s ▁( or ▁R DN s ) ▁a ims ▁to ▁get ▁the ▁joint ▁probability ▁distribution ▁over ▁the ▁variables ▁of ▁a ▁dataset ▁represented ▁in ▁the ▁rel ational ▁domain . ▁They ▁are ▁based ▁on ▁Dep endency ▁Network s ▁( or ▁D N s ) ▁and ▁extend ▁them ▁to ▁the ▁rel ational ▁setting . ▁R DN s ▁have ▁efficient ▁learning ▁methods ▁where ▁a ▁R DN ▁can ▁learn ▁the ▁parameters ▁independently , ▁that ▁is , ▁the ▁conditional ▁probability ▁distributions ▁can ▁be ▁estimated ▁separately . ▁Since ▁there ▁may ▁be ▁some ▁incons isten cies ▁due ▁to ▁the ▁independently ▁learning ▁method , ▁R DN s ▁use ▁Gib bs ▁sampling ▁to ▁recover ▁joint ▁distribution , ▁like ▁D N s . ▁ ▁Un like ▁Dep endency ▁Network s , ▁R DN s ▁need ▁three ▁graphs ▁to ▁fully ▁represent ▁them . ▁ ▁Data ▁graph : ▁It ' s ▁a ▁graph ▁whose ▁nodes ▁represent ▁objects ▁from ▁the ▁data ▁set ▁and ▁edges ▁represent ▁dependencies ▁between ▁objects . ▁Each ▁of ▁the ▁objects ▁and ▁edges ▁receives ▁a ▁type ▁and ▁each ▁of ▁the ▁objects ▁has ▁an ▁attribute ▁set . ▁Model ▁graph : ▁It ▁is ▁a ▁graph ▁of ▁a ▁higher ▁level , ▁more ▁specifically , ▁at ▁the ▁level ▁of ▁types . ▁Thus , ▁nodes ▁represent ▁attributes ▁of ▁a ▁given ▁type ▁and ▁edges ▁represent ▁dependencies ▁between ▁attributes ▁of ▁the ▁same ▁type ▁or ▁between ▁attributes ▁of ▁different ▁types . ▁Each ▁node ▁is ▁associated ▁with ▁a ▁probability ▁distribution ▁condition ed ▁to ▁its ▁parent ▁nodes . ▁The ▁model ▁graph ▁makes ▁no ▁assumptions ▁about ▁the ▁data ▁set