In this paper, we consider the problem of filtering in relational
Hidden Markov models. We present a compact representation for
such models and an associated logical particle filtering algorithm. Each
particle contains a logical formula that describes a set of states. The
algorithm updates the formulae as new observations are received. Since
a single particle tracks many states, this filter can be more accurate
than a traditional particle filter in high dimensional state spaces, as we
demonstrate in experiments.
標簽：
relational
filtering
consider
problem
上傳時間：
2016-01-02
上傳用戶：海陸空653
