Exact finite dimensional filters for certain exponential functionals of Gaussian state space processes
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In this paper, we derive finite dimensional filters for certain exponential functionals of the state of a continuous-time linear Gaussian process. Apart from being of mathematical interest, these new filters have applications in the state reconstruction of doubly-stochastic autoregressive processes. We also derive similar filters for exponential functionals of the state of nonlinear Benes systems.Keywords:
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In this paper we investigate a link between state- space models and Gaussian Processes (GP) for time series modeling and forecasting. In particular, several widely used state- space models are transformed into continuous time form and corresponding Gaussian Process kernels are derived. Experimen- tal results demonstrate that the derived GP kernels are correct and appropriate for Gaussian Process Regression. An experiment with a real world dataset shows that the modeling is identical with state-space models and with the proposed GP kernels. The considered connection allows the researchers to look at their models from a different angle and facilitate sharing ideas between these two different modeling approaches.
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A stochastic systems framework is established for the formulation of call admission control (CAC) and routing control (RC) problems in loss networks. The state process of the underlying system is a piecewise deterministic Markov process (PDMP) evolving deterministically between random event instants at which times the state jumps to another state value. The random events in the system correspond to the arrival of call requests or the departure of connections. The system state process is hybrid since it possesses positive integer and positive real valued components. The resulting NETCAD stochastic state space systems framework permits the formulation and analysis of centralized optimal stochastic control with respect to specified utility functions, see (Z. Ma et al., 2006)
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This paper constructs a finite state abstraction of a possibly continuous-time and infinite state model in two steps. First, a finite external signal space is added, generating a so called $\Phi$-dynamical system. Secondly, the strongest asynchronous $l$-complete approximation of the external dynamics is constructed. As our main results, we show that (i) the abstraction simulates the original system, and (ii) bisimilarity between the original system and its abstraction holds, if and only if the original system is $l$-complete and its state space satisfies an additional property.
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