We develop a state space framework for estimating term structure models, where latent Markovian state variables are mapped non-linearly into observable market data. The measurement equation of our framework is explicitly constructed such that it takes raw market prices and rates as direct inputs. We thus avoid entirely, the need for data preprocessing, such as the use of ad hoc interpolation an...

In this paper we introduce the globally gated Markovian limited service discipline in the cyclic polling model. Under this policy at most K customers are served during the server visit to a station among the customers that are present at the start of the actual polling cycle. Here the random limit K is the actual value of a finite state Markov chain assigned to the actual station. The model ena...

This paper addresses the realization of almost sure synchronization problem for a new array stochastic networks associated with delay and Lévy noise via event-triggered control. The coupling structure network is governed by continuous-time homogeneous Markov chain. nodes in communicate each other update their information only at discrete-time instants so that workload can be minimized. Under fr...

We tested whether and to what extent a remote sensing net primary productivity (NPP) estimation model based on the moderate resolution imaging spectroradiometer (MODIS) data and GIS modelled shortwave radiation can be used to estimate NPP in a Southern African Savanna landscape. To accomplish this, we compared the results of our model with results obtained using an established rainfall-NPP regr...

This is a survey of results on universal algorithms for classification and prediction stationary processes. The problems include discovering the order k-step Markov chain, determining memory words in finitarily Markovian processes estimating entropy an unknown process. cover both discrete real valued variety situations. Both forward backward are discussed with emphasis being pointwise results. ...

In this paper we propose a traffic model for delivering scalable video encoded with multiple layers on heterogeneous networks. The model is based on Markovian arrival process with marked transitions. The state of the underlying Markov chain of the video arrival process is derived from the correlation feature found in the video data. The base layer and enhancement layer video frame size pairs ar...

We revisit the challenging problem of finding an efficient Monte Carlo (MC) algorithm solving the constrained evolution equations for the initial-state QCD radiation. The type of the parton (quark, gluon) and the energy fraction x of the parton exiting emission chain (entering hard process) are predefined, i.e. constrained throughout the evolution. Such a constraint is mandatory for any realist...

We show how some graph theoretical arguments may be used to reduce the complexity of the computation of the steady-state distribution of Markov chain. We consider the directed graph associated to a Markov chain derived from a Stochastic Automata Network (SAN). The structural properties of the automata are used to establish new various results. First, we establish the complexity of the resolutio...

Adaptive Markov Chain Monte Carlo (MCMC) algorithms attempt to ‘learn’ from the results of past iterations so the Markov chain can converge quicker. Unfortunately, adaptive MCMC algorithms are no longer Markovian, so their convergence is difficult to guarantee. In this paper, we develop new diagnostics to determine whether the adaption is still improving the convergence. We present an algorithm...