A probabilistic model of human control behaviour is described. It assumes that human behaviour can be represented by switching among a number of relatively simple behaviours. The model structure is closely related to the Hidden Markov Models (HMMs) commonly used for speech recognition. An HMM with context-dependent transition functions switching between linear control laws is identified from ex...

* Research supported in part by National Nature Science Foundation of China Abstract A suboptimal approach to the d( 0 d ≥ ) step fixed-lag smoothing problem for Markovian switching systems is presented. Multiple Model Estimation techniques have been widely used in solving state estimation problems of these systems. We demonstrated that the mode probability of each fixed-lag smoother at time k-...

The switching model is a Markov chain approach to sample graphs with fixed degree sequence uniformly at random. The recently invented Curveball algorithm [35] for bipartite graphs applies several switches simultaneously (‘trades’). Here, we introduce Curveball algorithms for simple (un)directed graphs which use single or simultaneous trades. We show experimentally that these algorithms converge...

—A packet forwarding protocol based on terminals’ Markov mobility model (TMM) is proposed for coal mine poor connectivity in opportunistic networks since its complex environment. First, a Markov model is established for predicting the probability of terminals’ encounter. Then the network-delay in the condition of next selected relay-terminal (NDT), the probability of terminal encounter with de...

This paper presents a hybrid localization method designed for environments having the structure of a network (road networks, sewerage networks, underground mines, etc.. .). The method, which views localization as a problem of state estimation in a switching environment, combines the exibility and robustness of Markov localization with the accuracy and eeciency of Kalman ltering. This is achieve...

Label switching is a well-known phenomenon that occurs in MCMC outputs targeting the parameters’ posterior distribution of many latent variable models. Although its appearence is necessary for the convergence of the simulated Markov chain, it turns out to be a problem in the estimation procedure. In a recent paper, Papastamoulis and Iliopoulos (2010) introduced the Equivalence Classes Represent...

We show that the covariance function of a second-order stationary vector Markov regime switching time series has a vector ARMA(p; q) representation, where upper bounds for p and q are elementary functions of the number of regimes. These bounds apply to vector Markov regime switching processes with both mean-variance and autoregressive switching. This result yields an easily computed method for ...

We develop a continuous-time dynamic model of competition with switching costs to show that in a relatively simple Markov Perfect equilibrium, the dominant …rm concedes market share by charging higher prices than the smaller …rm. In the short-run, switching costs might have two types of anti-competitive e¤ects: …rst, higher switching costs imply a slower transition to a symmetric market structu...

This work focuses on regime-switching jump diffusions, which include three classes of random processes, Brownian motions, Poisson processes, and Markov chains. First, a scalar linear system is treated as a benchmark model. Then stabilization of systems with one-sided linear growth is considered. Next, nonlinear systems that have a finite explosion time are treated, in which regularization (expl...