an initial distribution over the features "now(time this is belief network with all variables being time variables specification of the dynamics we define the how the variables now (time depend on variables now and the previous time (time )in such way that the graph is acyclic probdbn py -dynamic belief networks from probvariables import variable from probgraphicalmodels import graphicalmodelbeliefnetwork from probfactors import probfactorcpd from probve import ve from display import displayable from utilities import dict_union class dbnvariable(variable)""" random variable that incorporates the stage (time variable can have both name and an index the index defaults to ""def __init__(self,name,domain=[false,true],index= )variable __init__(self, "{name} {index}",domainself basename name self domain domain self index index self previous none def __lt__(self,other)if self name !other namereturn self name<other name elsereturn self index<other index def __gt__(self,other)return other<self def variable_pair(name,domain=[false,true])"""returns variable and its predecessor this is used to define -stage dbns if the name is xit returns the pair of variables x_prev,x_now""var_now dbnvariable(name,domain,index='now'var_prev dbnvariable(name,domain,index='prev'var_now previous var_prev return var_prevvar_now factorrename is factor that is the result renaming the variables in the factor it takes factorfacand {new olddictionarywhere new is the name of variable in the resulting factor and old is the corresponding name in fac this assumes that the all variables are renamed version june