Complexity of existing mathematical models inhibits the analysis and the automatic animation of human and animal motion. Current techniques rely on numerical simulations of highly nonlinear differential equations in highdimensional spaces. A motion with such large-scale dynamics is difficult to control because of the sheer size of its control space. We are developing a method for constructing s...

In this paper we focus on one of the most important issues for autonomous mobile robots: their ability to localize themselves safely and reliably within their environments. We propose a probabilistic franiework for modelling the robot’s state and sensory information based on a Switching State-Space Model. The proposed framework generalizes two of the most successful probabilistic model families...

Model-checking offers a potential for push-button verification. Abstraction is often used to combat the state-space explosion problem and focus the analysis on relevant properties. However, in many such cases, it is difficult to interpret the results of verification on an abstract system with respect to a concrete one. In this paper we present an abstract model-checking approach that guarantees...

We consider the verification of algorithms expressed in the Heard-Of Model, a round-based computational model for fault-tolerant distributed computing. Rounds in this model are communication-closed, and we show that every execution recording individual events corresponds to a coarser-grained execution based on global rounds such that the local views of all processes are identical in the two exe...

every year, for realizing the goals of five-year state civil development plan, a great part of state credits and financial resources are spent for investment in national projects, whilst most of national projects are performed with considerable delay. essential objectives of applying this research is design and explanaion of national projects managers’ competencies model, focusing on risk that ...

A prevalent problem in statistical signal processing, applied statistics, and time series analysis is the calculation of the smoothed posterior distribution, which describes the uncertainty associated with a state, or a sequence of states, conditional on data from the past, the present, and the future. The aim of this paper is to provide a rigorous foundation for the calculation, or approximati...

Normalizing latent-variable models in empirical work has sometimes more influence on statistical inference than commonly appreciated. In this paper, I show how relating non-identification to an invariance property of the likelihood function under certain groups of parameter transformations helps understanding the influence of normalization on inference and can guide the choice of identifying re...

Most studies tackling hysteresis identification in the technical literature follow white-box approaches, i.e. they rely on the assumption that measured data obey a specific hysteretic model. Such an assumption may be a hard requirement to handle in real applications, since hysteresis is a highly individualistic nonlinear behaviour. The present paper adopts a black-box approach based on nonlinea...

We introduce a new statistical model for time series that iteratively segments data into regimes with approximately linear dynamics and learnsthe parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models -- hidden Markov models and linear dynamical systems -- and is closely related to models that are widely used in ...