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Stochastic process

About: Stochastic process is a research topic. Over the lifetime, 31227 publications have been published within this topic receiving 898736 citations. The topic is also known as: random process & stochastic processes.


Papers
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Journal ArticleDOI
TL;DR: This paper aims to design a state estimator to estimate the network states such that, for all admissible parameter uncertainties and time-varying delays, the dynamics of the estimation error is guaranteed to be globally exponentially stable in the mean square.
Abstract: This paper is concerned with the problem of state estimation for a class of discrete-time coupled uncertain stochastic complex networks with missing measurements and time-varying delay. The parameter uncertainties are assumed to be norm-bounded and enter into both the network state and the network output. The stochastic Brownian motions affect not only the coupling term of the network but also the overall network dynamics. The nonlinear terms that satisfy the usual Lipschitz conditions exist in both the state and measurement equations. Through available output measurements described by a binary switching sequence that obeys a conditional probability distribution, we aim to design a state estimator to estimate the network states such that, for all admissible parameter uncertainties and time-varying delays, the dynamics of the estimation error is guaranteed to be globally exponentially stable in the mean square. By employing the Lyapunov functional method combined with the stochastic analysis approach, several delay-dependent criteria are established that ensure the existence of the desired estimator gains, and then the explicit expression of such estimator gains is characterized in terms of the solution to certain linear matrix inequalities (LMIs). Two numerical examples are exploited to illustrate the effectiveness of the proposed estimator design schemes.

212 citations

Posted Content
TL;DR: A* sampling as mentioned in this paper is a generic sampling algorithm that searches for the maximum of a Gumbel process using A* search, which makes more efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling based algorithms.
Abstract: The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem In this work, we show how sampling from a continuous distribution can be converted into an optimization problem over continuous space Central to the method is a stochastic process recently described in mathematical statistics that we call the Gumbel process We present a new construction of the Gumbel process and A* sampling, a practical generic sampling algorithm that searches for the maximum of a Gumbel process using A* search We analyze the correctness and convergence time of A* sampling and demonstrate empirically that it makes more efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms

212 citations

Journal ArticleDOI
TL;DR: In this paper, the authors considered the consensus problem for a team of second-order mobile agents communicating via a network with noise, variable delays and occasional packet losses and proposed an approach to designing consensus protocol and numerical examples are given to illustrate the results.
Abstract: The consensus problem is considered for a team of second-order mobile agents communicating via a network with noise, variable delays and occasional packet losses. A queuing mechanism is applied and the switching process of the interaction topology of the network is modeled as a Bernoulli random process. In such a framework, a necessary and sufficient condition is presented for the mean-square robust consensus. Moreover, a necessary and sufficient condition of the solvability of the mean-square robust consensus problem is established. An approach to designing consensus protocol is proposed and numerical examples are given to illustrate the results.

212 citations

Journal ArticleDOI
TL;DR: New asymptotics formulas for the mean exit time from an almost stable domain of a discrete-time Markov process are obtained and an original fast simulation method is proposed based on the classical Robbins-Monroe algorithm.
Abstract: New asymptotics formulas for the mean exit time from an almost stable domain of a discrete-time Markov process are obtained. An original fast simulation method is also proposed. The mathematical background involves the large deviation theorems and approximations by a diffusion process. We are chiefly concerned with the classical Robbins-Monroe algorithm. The validity of the results are tested on examples from the ALOHA system (a satellite type communication algorithm).

212 citations

Book
01 Jan 2004
TL;DR: This volume describes the essential tools and techniques of statistical signal processing and offers a wide variety of examples of the most popular random process models and their basic uses and properties.
Abstract: This volume describes the essential tools and techniques of statistical signal processing. At every stage, theoretical ideas are linked to specific applications in communications and signal processing. The book begins with an overview of basic probability, random objects, expectation, and second-order moment theory, followed by a wide variety of examples of the most popular random process models and their basic uses and properties. Specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, and other processing of signals are interspersed throughout the text.

212 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023159
2022355
2021985
20201,151
20191,119
20181,115