@article{BettenbuehlRusconiEngbertetal.2012,
  author    = {Bettenb{\"u}hl, Mario and Rusconi, Marco and Engbert, Ralf and Holschneider, Matthias},
  title     = {Bayesian selection of Markov Models for symbol sequences application to microsaccadic eye movements},
  series = {PLoS one},
  volume    = {7},
  journal   = {PLoS one},
  number    = {9},
  publisher = {PLoS},
  address   = {San Fransisco},
  issn      = {1932-6203},
  doi       = {10.1371/journal.pone.0043388},
  pages     = {10},
  year      = {2012},
  abstract  = {Complex biological dynamics often generate sequences of discrete events which can be described as a Markov process. The order of the underlying Markovian stochastic process is fundamental for characterizing statistical dependencies within sequences. As an example for this class of biological systems, we investigate the Markov order of sequences of microsaccadic eye movements from human observers. We calculate the integrated likelihood of a given sequence for various orders of the Markov process and use this in a Bayesian framework for statistical inference on the Markov order. Our analysis shows that data from most participants are best explained by a first-order Markov process. This is compatible with recent findings of a statistical coupling of subsequent microsaccade orientations. Our method might prove to be useful for a broad class of biological systems.},
  language  = {en}
}
@article{MakaravaBettenbuehlEngbertetal.2012,
  author    = {Makarava, Natallia and Bettenb{\"u}hl, Mario and Engbert, Ralf and Holschneider, Matthias},
  title     = {Bayesian estimation of the scaling parameter of fixational eye movements},
  series = {epl : a letters journal exploring the frontiers of physics},
  volume    = {100},
  journal   = {epl : a letters journal exploring the frontiers of physics},
  number    = {4},
  publisher = {EDP Sciences},
  address   = {Mulhouse},
  issn      = {0295-5075},
  doi       = {10.1209/0295-5075/100/40003},
  pages     = {6},
  year      = {2012},
  abstract  = {In this study we re-evaluate the estimation of the self-similarity exponent of fixational eye movements using Bayesian theory. Our analysis is based on a subsampling decomposition, which permits an analysis of the signal up to some scale factor. We demonstrate that our approach can be applied to simulated data from mathematical models of fixational eye movements to distinguish the models' properties reliably.},
  language  = {en}
}