In this paper, a new state-space method for language modeling is presented. The complexity of the model is controlled by choosing the dimension of the state instead of the smoothing and back-off methods common in n-gram modeling. The model complexity also controls the generalization ability of the model, allowing the model to handle similar words in similar manner. We compare the state-space mo...

We are often interested in finding out if a given predicate on states of a system can be satisfied in any or all of the systems reachable states. To determine this, one approach is to calculate the complete set of reachable states and evaluate the predicate on each of these states. To know which states we have already examined and which we have yet to examine—and thus knowing when we have exami...

The sweep-line method exploits intrinsic progress in concurrent systems to alleviate the state explosion problem in explicit state model checking. The concept of progress makes it possible to delete states from the memory during state space exploration and thereby reduce peak memory usage. The contribution of this paper is twofold. First, we provide a coherent presentation of the sweep-line the...

In the context of state space planning, a mutex pair is a pair of variable-value assignments that does not occur in any reachable state. Detecting mutex pairs is a problem that has been addressed frequently in the planning literature. In this paper, we present the Missing Mass Method (MMM)—a new efficient and domain-independent method for mutex pair detection, based on sampling reachable states...

In a first order autoregressive model with drift, we derive the likelihood ratio test for a unit root against the stationary alternative. We also derive the test in a state space model with trend. Finite sample and asymptotic critical values are obtained by Monte Carlo simulations. A simulation study investigates the power performance of the likelihood ratio test and we also examine how a bias ...