Auswahl der wissenschaftlichen Literatur zum Thema „Poisson-Distributed observations“
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Zeitschriftenartikel zum Thema "Poisson-Distributed observations"
Ades, M., P. E. Caines und R. P. Malhame. „Stochastic optimal control under Poisson-distributed observations“. IEEE Transactions on Automatic Control 45, Nr. 1 (2000): 3–13. http://dx.doi.org/10.1109/9.827351.
Der volle Inhalt der QuelleHakulinen, Timo, und Tadeusz Dyba. „Precision of incidence predictions based on poisson distributed observations“. Statistics in Medicine 13, Nr. 15 (15.08.1994): 1513–23. http://dx.doi.org/10.1002/sim.4780131503.
Der volle Inhalt der QuelleKirmani, S. N. U. A., und Jacek Wesołowski. „Time spent below a random threshold by a Poisson driven sequence of observations“. Journal of Applied Probability 40, Nr. 3 (September 2003): 807–14. http://dx.doi.org/10.1239/jap/1059060907.
Der volle Inhalt der QuelleKirmani, S. N. U. A., und Jacek Wesołowski. „Time spent below a random threshold by a Poisson driven sequence of observations“. Journal of Applied Probability 40, Nr. 03 (September 2003): 807–14. http://dx.doi.org/10.1017/s0021900200019756.
Der volle Inhalt der QuelleTaylor, Greg. „EXISTENCE AND UNIQUENESS OF CHAIN LADDER SOLUTIONS“. ASTIN Bulletin 47, Nr. 1 (12.08.2016): 1–41. http://dx.doi.org/10.1017/asb.2016.23.
Der volle Inhalt der QuelleLi, Li. „The GLR Chart for Poisson Process with Individual Observations“. Advanced Materials Research 542-543 (Juni 2012): 42–46. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.42.
Der volle Inhalt der QuelleCollings, Bruce J., und Barry H. Margolin. „Testing Goodness of Fit for the Poisson Assumption When Observations are Not Identically Distributed“. Journal of the American Statistical Association 80, Nr. 390 (Juni 1985): 411–18. http://dx.doi.org/10.1080/01621459.1985.10478132.
Der volle Inhalt der QuelleBülow, Tanja, Ralf-Dieter Hilgers und Nicole Heussen. „Confidence interval comparison: Precision of maximum likelihood estimates in LLOQ affected data“. PLOS ONE 18, Nr. 11 (02.11.2023): e0293640. http://dx.doi.org/10.1371/journal.pone.0293640.
Der volle Inhalt der QuelleLauderdale, Benjamin E. „Compound Poisson—Gamma Regression Models for Dollar Outcomes That Are Sometimes Zero“. Political Analysis 20, Nr. 3 (2012): 387–99. http://dx.doi.org/10.1093/pan/mps018.
Der volle Inhalt der QuelleGnedin, Alexander V. „Optimal Stopping with Rank-Dependent Loss“. Journal of Applied Probability 44, Nr. 04 (Dezember 2007): 996–1011. http://dx.doi.org/10.1017/s0021900200003697.
Der volle Inhalt der QuelleDissertationen zum Thema "Poisson-Distributed observations"
Dyba, Tadeusz. „Precision of cancer incidence predictions based on poisson distributed observations“. Helsinki : University of Helsinki, 2000. http://ethesis.helsinki.fi/julkaisut/val/tilas/vk/dyba/.
Der volle Inhalt der QuelleIufereva, Olga. „Algorithmes de filtrage avec les observations distribuées par Poisson“. Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. https://theses.hal.science/tel-04720020.
Der volle Inhalt der QuelleFiltering theory basically relates to optimal state estimation in stochastic dynamical systems, particularly when faced with partial and noisy data. This field, closely intertwined with control theory, focuses on designing estimators doing real-time computation while maintaining an acceptable level of accuracy as measured by the mean square error. The necessity for such estimates becomes increasingly critical with the proliferation of network-controlled systems, such as autonomous vehicles and complex industrial processes, where the observation processes are subject to randomness in transmission and this gives rise to varying information patterns under which the estimation must be carried out.This thesis addresses the important task of state estimation in continuous-time stochastic dynamical systems when the observation process is available only at some discrete time instants governed by a random process. By adapting classical estimation methods, we derive equations for optimal state estimator, explore their properties and practicality, and propose and evaluates sub-optimal alternatives, showcasing parallels to the existing techniques within the classical estimation domain when applied to Poisson-distributed observation processes.The study covers three classes of mathematical models for the continuous-time dynamical system and the discrete observation process. First, we consider Ito-stochastic differential equations with Lipschitz drift terms and constant diffusion coefficient, whereas the lower-dimensional discrete observation process comprises the nonlinear mapping of the state and additive Gaussian noise. We propose easy-to-implement continuous-discrete suboptimal state estimators for this system class. Assuming that a Poisson counter governs discrete times at which the observations are available, we compute the expectation or error covariance process. Analysis is carried out to provide conditions for boundedness of the error covariance process, as well as, the dependence on the mean sampling rate.Secondly, we consider the dynamical systems described by continuous-time Markov chains with finite state space, and the observation process is obtained by discretizing a conventional stochastic process driven by a Wiener process. For this case, the $L_1$-convergence of the derived optimal estimator to the classical (purely continuous) optimal estimator (Wonham filter) is shown with respect to increasing intensity of Poisson processes.Lastly, we study continuous-discrete particle filters for Ornstein-Uhlenbeck processes with discrete observations described by linear functions of state and additive Gaussian noise. Particle filters have gained a lot of interest for state estimation in large-scale models with noisy measurements where the computation of optimal gain is either computationally expensive or not entirely feasible due to complexity of the dynamics. In this thesis, we propose continuous-discrete McKean–Vlasov type diffusion processes, which serve as the mean-field model for describing the particle dynamics. We study several kinds of mean-field processes depending on how the noise terms are included in mimicking the state process and the observation model. The resulting particles are coupled through empirical covariances which are updated at discrete times with the arrival of new observations. With appropriate analysis of the first and second moments, we show that under certain conditions on system parameters, the performance of the particle filters approaches the optimal filter as the number of particles gets larger
Buchteile zum Thema "Poisson-Distributed observations"
Pahlajani, Chetan D., Indrajeet Yadav, Herbert G. Tanner und Ioannis Poulakakis. „Decision-Making Accuracy for Sensor Networks with Inhomogeneous Poisson Observations“. In Distributed Autonomous Robotic Systems, 177–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73008-0_13.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Poisson-Distributed observations"
Ralte, Vanlalruata, Amitalok J. Budkuley und Stefano Rini. „Distributed Sampling for the Detection of Poisson Sources Under Observation Erasures“. In 2024 IEEE International Symposium on Information Theory (ISIT), 3510–15. IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619638.
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