The ignoring problem refers to the fact that some actions may be infinitely postponed by a state space search algorithm that makes use of partial order reduction (POR). The prevention of this phenomenon is mandatory if one wants to verify more elaborate properties than the deadlock freeness, e.g., safety or liveness properties. We present in this work some solutions to this problem. In order to...

An extended state space (ESS) model, familiar in subspace identification theory, is used for the development of a model based predictive control algorithm for linear model structures. In the ESS model, the state vector consists of system outputs, which eliminates the need for a state estimator. A framework for model based predictive control is presented. Both general linear state space model st...

Model checking by exhaustive state space enumeration is one of the most developed analysis methods for distributed event systems. Its main problem—the size of the state spaces—has been addressed by various reduction methods. Complex systems tend to consist of loosely connected modules, which may perform internal tasks in parallel. The possible interleavings of these parallel tasks easily leads ...

We present the implementation of the trace theory in a new model checking tool framework, POEM, that has a strong emphasis on Partial Order Methods. A tree structure is used to store trace systems, which allows sharing common prefixes among traces and therefore, reduces memory cost. This structure is easy to extend to incorporate additional features. Two applications are shown in the paper: An ...

Gaussian process (GP) is a popular non-parametric model for Bayesian inference. However, the performance of GP is often limited in temporal applications, where the input–output pairs are sequentiallyordered, and often exhibit time-varying non-stationarity and heteroscedasticity. In this work, we propose two particle-based GP approaches to capture these distinct temporal characteristics. Firstly...

To analyze whole-genome genetic data inherited in families, the likelihood is typically obtained from a Hidden Markov Model (HMM) having a state space of 2 hidden states where n is the number of meioses or edges in the pedigree. There have been several attempts to speed up this calculation by reducing the state-space of the HMM. One of these methods has been automated in a calculation that is m...

The important feature of temporal model checking is the generation of counterexamples. In the report, the requirements for generation of counterexample (called critical tree) in model checking of CSM systems are described. The output of TempoRG model checker for QsCTL logic (a version of CTL) is presented. A contradiction between counterexample generation and state space reduction is commented.

We provide a language for formulating a range of state space models with response densities within the exponential family. The described methodology is implemented in the R-package sspir. A state space model is specified similarly to a generalized linear model in R, and then the time-varying terms are marked in the formula. Special functions for specifying polynomial time trends, harmonic seaso...

This article explores an alternative state space representation for ARIMA models to that usually advocated. The alternative representation has minimal state order. More importantly, it has more convenient Kalman filter convergence properties. This convergence reveals the concrete connection between classical infinite sample representations based on lag polynomials and the recursive Kalman filte...