A Bayesian approach to model checking biological systems

@INPROCEEDINGS{DBLP:conf/cmsb/JhaCLLPZ09,
	pdf = {pub/bayesmcbio.pdf},
	TR = {DBLP:conf/cmsb/JhaCLLPZ09:TR},

  author    = {Sumit Kumar Jha and
               Edmund Clarke and
               Christopher Langmead and
               Axel Legay and
               Andr{\'e} Platzer and
               Paolo Zuliani},
  title     = {A {Bayesian} Approach to Model Checking
               Biological Systems},
  booktitle = {CMSB},
  year      = {2009},
  pages     = {218-234},
  editor    = {Pierpaolo Degano and
               Roberto Gorrieri},
  longbooktitle = {Computational Methods in Systems
               Biology, 7th International Conference, CMSB
               2009, Bologna, Italy, Proceedings},
  publisher = {Springer},
  series    = {LNCS},
  volume    = {5688},
  doi       = {10.1007/978-3-642-03845-7_15},
  abstract  = {
    Recently, there has been considerable interest in the
    use of Model Checking for Systems Biology.
    Unfortunately, the state space of stochastic biological
    models is often too large for classical Model Checking
    techniques. For these models, a statistical approach to
    Model Checking has been shown to be an effective
    alternative. Extending our earlier work, we present the
    first algorithm for performing statistical Model
    Checking using Bayesian Sequential Hypothesis Testing.
    We show that our Bayesian approach outperforms current
    statistical Model Checking techniques, which rely on
    tests from Classical (aka Frequentist) statistics, by
    requiring fewer system simulations. Another advantage
    of our approach is the ability to incorporate prior
    Biological knowledge about the model being verified. We
    demonstrate our algorithm on a variety of models from
    the Systems Biology literature and show that it enables
    faster verification than state-of-the-art techniques,
    even when no prior knowledge is available.}
}```