@article{MarionMcInernyPageletal.2012,
  author    = {Marion, Glenn and McInerny, Greg J. and Pagel, J{\"o}rn and Catterall, Stephen and Cook, Alex R. and Hartig, Florian and O\&rsquo, and Hara, Robert B.},
  title     = {Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche},
  series = {JOURNAL OF BIOGEOGRAPHY},
  volume    = {39},
  journal   = {JOURNAL OF BIOGEOGRAPHY},
  number    = {12},
  publisher = {WILEY-BLACKWELL},
  address   = {HOBOKEN},
  issn      = {0305-0270},
  doi       = {10.1111/j.1365-2699.2012.02772.x},
  pages     = {2225 -- 2239},
  year      = {2012},
  abstract  = {The spatial distribution of a species is determined by dynamic processes such as reproduction, mortality and dispersal. Conventional static species distribution models (SDMs) do not incorporate these processes explicitly. This limits their applicability, particularly for non-equilibrium situations such as invasions or climate change. In this paper we show how dynamic SDMs can be formulated and fitted to data within a Bayesian framework. Our focus is on discrete state-space Markov process models which provide a flexible framework to account for stochasticity in key demographic processes, including dispersal, growth and competition. We show how to construct likelihood functions for such models (both discrete and continuous time versions) and how these can be combined with suitable observation models to conduct Bayesian parameter inference using computational techniques such as Markov chain Monte Carlo. We illustrate the current state-of-the-art with three contrasting examples using both simulated and empirical data. The use of simulated data allows the robustness of the methods to be tested with respect to deficiencies in both data and model. These examples show how mechanistic understanding of the processes that determine distribution and abundance can be combined with different sources of information at a range of spatial and temporal scales. Application of such techniques will enable more reliable inference and projections, e.g. under future climate change scenarios than is possible with purely correlative approaches. Conversely, confronting such process-oriented niche models with abundance and distribution data will test current understanding and may ultimately feedback to improve underlying ecological theory.},
  language  = {en}
}
@phdthesis{Makarava2012,
  author    = {Makarava, Natallia},
  title     = {Bayesian estimation of self-similarity exponent},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-64099},
  school      = {Universit{\"a}t Potsdam},
  year      = {2012},
  abstract  = {Estimation of the self-similarity exponent has attracted growing interest in recent decades and became a research subject in various fields and disciplines. Real-world data exhibiting self-similar behavior and/or parametrized by self-similarity exponent (in particular Hurst exponent) have been collected in different fields ranging from finance and human sciencies to hydrologic and traffic networks. Such rich classes of possible applications obligates researchers to investigate qualitatively new methods for estimation of the self-similarity exponent as well as identification of long-range dependencies (or long memory). In this thesis I present the Bayesian estimation of the Hurst exponent. In contrast to previous methods, the Bayesian approach allows the possibility to calculate the point estimator and confidence intervals at the same time, bringing significant advantages in data-analysis as discussed in this thesis. Moreover, it is also applicable to short data and unevenly sampled data, thus broadening the range of systems where the estimation of the Hurst exponent is possible. Taking into account that one of the substantial classes of great interest in modeling is the class of Gaussian self-similar processes, this thesis considers the realizations of the processes of fractional Brownian motion and fractional Gaussian noise. Additionally, applications to real-world data, such as the data of water level of the Nile River and fixational eye movements are also discussed.},
  language  = {en}
}