In this section, we describe the general Gibbs sampler for our nonparametric model. The state space of our chain is the topic indices assigned to all tokens z = {zc,t,n} and topic shifts assigned to all turns l = {lc,t}. In order to obtain zc,t,n we need to know the path assigned for token wc,t,n through the hierarchy which includes kT c,t,n, k S c,s,j and k C c,i. For ease of reference, the me...

Models of dynamical systems based on predictive state representations (PSRs) are defined strictly in terms of observable quantities, in contrast with traditional models (such as Hidden Markov Models) that use latent variables or statespace representations. In addition, PSRs have an effectively infinite memory, allowing them to model some systems that finite memory-based models cannot. Thus far,...

This paper considers a state space model with integrated latent variables. The model provides an effective framework to specify, test and extract common stochastic trends for a set of integrated time series. The model can be readily estimated by the standard Kalman filter, whose asymptotics are fully developed in the paper. In particular, we establish the consistency and asymptotic mixed normal...

The purpose of this paper is to derive conditions for absolute stability as well as existence of solutions for multivariable Lur’e-type feedback systems whose linear part is expressed by a descriptor system. The nonlinearities are uncertain and satisfy multivariable sector conditions, or a part of the nonlinearities satisfies a norm bounded condition. Thus, the systems can be refered to as mult...

Population protocols are a popular model of distributed computing, in which n agents with limited local state interact randomly, and cooperate to collectively compute global predicates. Inspired by recent developments in DNA programming, an extensive series of papers, across different communities, has examined the computability and complexity characteristics of this model. Majority, or consensu...

POMDPs are a useful model for decision making in systems with uncertain states. One of the core tasks in a POMDP is the monitoring task, in which the belief state (i.e. the probability distribution over system states) is updated based on incomplete and noisy observations. This can be a hard problem in complex real-world systems due to the often very large state space. In this article, we explor...

H altitude or impulse response matrix I identity matrix j dummy index M Mach number R correlation coefficient S matrix of singular values STBL left stabiliser position STBR right stabiliser position t, Δt time, time step T projected state matrix multiplied by the instrument TEFL left trailing-edge flap deflection TEFR right trailing-edge flap deflection u system input vector U matrix of past or...

Multi-process handshaking is a generalization of ordinary handshake communication between two processes. It allows an arbitrary number of processes to be synchronized in a common handshake, for the purpose of carrying out a joint action, consisting of a sequential statement that is executed in the combined state space of the processes participating in the handshake. The statement updates the lo...

The convergence properties of recently developed recursive subspace identification methods are investigated in this paper. The algorithms operate on the basis of instrumental variable (IV) versions of the propagator method for signal subspace estimation. It is proved that, under suitable conditions on the input signal and the system, the considered recursive subspace identification algorithms c...

SUMMARY We present a uniied semiparametric Bayesian approach based on Markov random eld priors for analyzing the dependence of multicategorical response variables on time, space and further covariates. The general model extends dynamic, or state space, models for categorical time series and longitudinal data by including spatial eeects as well as nonlinear eeects of metrical covariates in exibl...