Capturing Structure Implicitly from Time-Series having Limited Data

Preprint English OPEN
Emaasit, Daniel; Johnson, Matthew;
  • Subject: Statistics - Machine Learning | Computer Science - Learning

Scientific fields such as insider-threat detection and highway-safety planning often lack sufficient amounts of time-series data to estimate statistical models for the purpose of scientific discovery. Moreover, the available limited data are quite noisy. This presents a... View more
  • References (24)
    24 references, page 1 of 3

    Brochu, E., Cora, V. M., and De Freitas, N. (2010). A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and CERT-Division. Software Engineering Institute. divisions/cert/index.cfm. Accessed: January 30, 2018.

    Duvenaud, D. (2014). Automatic model construction with Gaussian processes. PhD thesis, University of Cambridge.

    Emaasit, D. and Paz, A. (2018). Simultaneous estimation of flexible models and associated hyperparameters: An application to activity-duration modeling. In Transportation Research Board 97th Annual Meeting. Washington DC: Transportation Research Board.

    FHWA. Highway Statistics Series. https:// statistics.cfm. Accessed: January 30, 2018.

    Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553):452- 459.

    Gheyas, I. A. and Abdallah, A. E. (2016). Detection and prediction of insider threats to cyber security: a systematic literature review and meta-analysis. Big Data Analytics, 1(1):6.

    Glasser, J. and Lindauer, B. (2013). Bridging the gap: A pragmatic approach to generating insider threat data. In Security and Privacy Workshops (SPW), 2013 IEEE, pages 98-104. IEEE.

    Greitzer, F. L. and Ferryman, T. A. (2013). Methods and metrics for evaluating analytic insider threat tools. In Security and Privacy Workshops (SPW), 2013 IEEE, pages 90-97. IEEE.

    Hjort, N. L., Holmes, C., Mu¨ller, P., and Walker, S. G. (2010). Bayesian nonparametrics, volume 28. Cambridge University Press.

    Hoffman, M. D. and Gelman, A. (2014). The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo. Journal of Machine Learning Research, 15(1):1593-1623.

  • Related Research Results (1)
  • Metrics
    No metrics available
Share - Bookmark