---
res:
  bibo_abstract:
  - Mathematical models are of fundamental importance in the understanding of complex
    population dynamics. For instance, they can be used to predict the population
    evolution starting from different initial conditions or to test how a system responds
    to external perturbations. For this analysis to be meaningful in real applications,
    however, it is of paramount importance to choose an appropriate model structure
    and to infer the model parameters from measured data. While many parameter inference
    methods are available for models based on deterministic ordinary differential
    equations, the same does not hold for more detailed individual-based models. Here
    we consider, in particular, stochastic models in which the time evolution of the
    species abundances is described by a continuous-time Markov chain. These models
    are governed by a master equation that is typically difficult to solve. Consequently,
    traditional inference methods that rely on iterative evaluation of parameter likelihoods
    are computationally intractable. The aim of this paper is to present recent advances
    in parameter inference for continuous-time Markov chain models, based on a moment
    closure approximation of the parameter likelihood, and to investigate how these
    results can help in understanding, and ultimately controlling, complex systems
    in ecology. Specifically, we illustrate through an agricultural pest case study
    how parameters of a stochastic individual-based model can be identified from measured
    data and how the resulting model can be used to solve an optimal control problem
    in a stochastic setting. In particular, we show how the matter of determining
    the optimal combination of two different pest control methods can be formulated
    as a chance constrained optimization problem where the control action is modeled
    as a state reset, leading to a hybrid system formulation.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Francesca
      foaf_name: Parise, Francesca
      foaf_surname: Parise
  - foaf_Person:
      foaf_givenName: John
      foaf_name: Lygeros, John
      foaf_surname: Lygeros
  - foaf_Person:
      foaf_givenName: Jakob
      foaf_name: Ruess, Jakob
      foaf_surname: Ruess
      foaf_workInfoHomepage: http://www.librecat.org/personId=4A245D00-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-1615-3282
  bibo_doi: 10.3389/fenvs.2015.00042
  bibo_volume: 3
  dct_date: 2015^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2296-665X
  dct_language: eng
  dct_publisher: Frontiers@
  dct_subject:
  - General Environmental Science
  dct_title: 'Bayesian inference for stochastic individual-based models of ecological
    systems: a pest control simulation study@'
...