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Results for Hidden Markov model

Publications & Outputs

  1. Estimating abundance from multiple sampling capture-recapture data via a multi-state multi-period stopover model

    Worthington, H., McCrea, R., King, R. & Griffiths, R., 1/12/2019, In: Annals of Applied Statistics. 13, 4, p. 2043-2064 22 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  2. Device-free human localization and tracking with UHF passive RFID tags: A data-driven approach

    Ruan, W., Sheng, Q. Z., Yao, L., Li, X., Falkner, N. J. G. & Yang, L., 15/02/2018, In: Journal of Network and Computer Applications. 104, p. 78-96 19 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  3. Machine learning in sentiment reconstruction of the simulated stock market

    Goykhman, M. & Teimouri, I., 15/02/2018, In: Physica A: Statistical Mechanics and its Applications. 492, p. 1729-1740 12 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  4. Estimating parametric semi-Markov models from panel data using phase-type approximations

    Titman, A., 03/2014, In: Statistics and Computing. 24, 2, p. 155-164 10 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review

  5. Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches

    Yao, L., Ruan, W., Sheng, Q. Z., Li, X. & Falkner, N. J. G., 1/01/2014, CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, p. 1799-1802 4 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  6. Semi-Markov models with phase-type sojourn distributions.

    Titman, A. C. & Sharples, L. D., 09/2010, In: Biometrics. 66, 3, p. 742-752 11 p.

    Research output: Contribution to Journal/MagazineJournal articlepeer-review