We present a new method for fighting the state space explosion of process algebraic specifications, by performing static analysis on an intermediate format: linear process equations (LPEs). Our method consists of two steps: (1) we reconstruct the LPE’s control flow, detecting control flow parameters that were introduced by linearisation as well as those already encoded in the original specifica...

Density matrices and the question of their entanglement have been studied very intensively during the last few years [1]. However the question of how these density matrices arise in the first place has received much less attention. It has been tacitly assumed that density matrices arise when a number of parties, separated in space, start by sharing a pure state, and then this state gets contami...

We study preferences for timing of resolution of objective uncertainty in a simple menu-choice model with two stages of information arrival. We characterize a general class of utility representations called hidden action representations, which interpret an intrinsic preference for timing of resolution of uncertainty as if an unobservable action is taken between the resolution of the two periods...

Dynamic models extend state space models to non-normal observations. This paper suggests a speci®c hybrid Metropolis±Hastings algorithm as a simple device for Bayesian inference via Markov chain Monte Carlo in dynamic models. Hastings proposals from the (conditional) prior distribution of the unknown, time-varying parameters are used to update the corresponding full conditional distributions. I...

In this paper a set of sufficient conditions is developed in terms of controllability and observability functions under which a given state space realization of a formal power series is minimal. Specifically, it is shown that positivity of these functions, in addition to a stability requirement and a few technical conditions, implies minimality. In doing so, connections are established between ...

Probabilistic models have been adopted for many computer vision applications, however inference in highdimensional spaces remains problematic. As the statespace of a model grows, the dependencies between the dimensions lead to an exponential growth in computation when performing inference. Many common computer vision problems naturally map onto the graphical model framework; the representation ...

We present a shallow embedding in PVS of a predicate transformer semantics of an imperative language suitable for reasoning about recursive procedures with parameters and local variables. We use the PVS dependent type mechanism for implementing program variables of different types. We use an uninterpreted state space and define the program variables behavior by means of certain tree functions t...

Modal logic represents knowledge that agents have about other agents’ knowledge. Probabilistic modal logic further captures probabilistic beliefs about probabilistic beliefs. Models in those logics are useful for understanding and decision making in conversations, bargaining situations, and competitions. Unfortunately, probabilistic modal structures are impractical for large real-world applicat...

In this contribution we present an improved indexbased a-posteriori probability (APP) decoding approach for variable-length encoded packetized data, where implicit residual source correlation is exploited for error protection. The proposed algorithm is based on a novel generalized two-dimensional state representation which leads to a three-dimensional trellis with unique state transitions. APP ...

The performance of Vector Controlled Induction Motor drive depends on the accuracy of rotor resistance which will vary with temperature and frequency. The MRAS approach using reactive power and flux as a state variable for rotor resistance estimation makes MRAS computationally simpler and easy to design. In this paper, Rotor Flux based MRAS (RF-MRAS) and Reactive Power based MRAS (RP-MRAS) for ...