reasoning under uncertainty """returns observation sequence for the state sequence""obsseq=[for state in stateseqnewobs {obs:sample_one({ : -hmm pobs[obs][state], :hmm pobs[obs][state]}for obs in hmm obsvarsobsseq append(newobsreturn obsseq def create_eg(hmm, )"""create an annotated example for horizon ""seq,obs simulate(hmm,nprint("true state sequence:",seqprint("sequence of observations:\ ",obshmmfilter hmmvefilter(hmmdist hmmfilter filter(obsprint("resulting distribution over states:\ ",dist dynamic belief networks dynamic belief network (dbnis belief network that extends in time there are number of ways that reasoning can be carried out in dbnincludingrolling out the dbn for some time periodand using standard belief network inference the latest time that needs to be in the rolled out network is the time of the latest observation or the time of query (whichever is laterthis allows us to observe any variables at any time and query any variables at any time this is covered in section an unrolled belief network may be very largeand we might only be interested in asking about "nowin this case we can just representing the variables "nowin this approach we can observe and query the current variables we can them move to the next time this does not allow for arbitrary historical queries (about the past or the future)but can be much simpler this is covered in section representing dynamic belief networks to specify dbnthink about the distribution now now will be represented as time each variable will have corresponding previous variablethese will be created together dynamic belief network consists ofa set of features variable is feature-time pair version june