We introduce and analyze Discriminative State-Space Models for forecasting nonstationary time series. We provide data-dependent generalization guarantees for learning these models based on the recently introduced notion of discrepancy. We provide an in-depth analysis of the complexity of such models. We also study the generalization guarantees for several structural risk minimization approaches...

We survey existing approaches to the formal verification of statecharts using model checking. Although the semantics and subset of statecharts used in each approach varies considerably, along with the model checkers and their specification languages, most approaches rely on translating the hierarchical structure into the flat representation of the input language of the model checker. This makes...

We describe a Markov latent state space (MLSS) model, where the latent state distribution is a decaying mixture over multiple past states. We present a simple sampling algorithm that allows to approximate such high-order MLSS with fixed time and memory costs.

This work focuses on the synthesis of finite-state machines (FSMs) by observing its input/output behaviors . Evolutionary approaches that have been proposed to solve this problem do not include strategies to escape from local optima, a typical problem found in simple evolutionary algorithms , particularly in the evolution of sequential machines. Simulations show that the proposed approach impro...

In this paper, the blind channel identification problem is formulated in a stochastic state space framework. Starting from a state space model we present a preprocessing step based on two orthogonal subspace projections. Using these orthogonal projections, we derive an algorithm for blind channel estimation which is insensitive to the spatial color of the noise. The performance of this new algo...

In agriculture Markov decision processes (MDPs) with finite state and action space are often used to model sequential decision making over time. For instance, states in the process represent possible levels of traits of the animal and transition probabilities are based on biological models estimated from data collected from the animal or herd. State space models (SSMs) are a general tool for mo...