The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Statistical inference for stochastic simulation models - theory and application
Abstract
Statistical models are the traditional choice to test scientific theories when observations, processes or boundary conditions are subject to stochasticity. Many important systems in ecology and biology, however, are difficult to capture with statistical models. Stochastic simulation models offer an alternative, but they were hitherto associated with a major disadvantage: their likelihood functions can usually not be calculated explicitly, and thus it is difficult to couple them to well-established statistical theory such as maximum likelihood and Bayesian statistics. A number of new methods, among them Approximate Bayesian Computing and Pattern-Oriented Modelling, bypass this limitation. These methods share three main principles: aggregation of simulated and observed data via summary statistics, likelihood approximation based on the summary statistics, and efficient sampling. We discuss principles as well as advantages and caveats of these methods, and demonstrate their potential for integrating stochastic simulation models into a unified framework for statistical modelling.
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Citations
Cites
Effective leadership and decision-making in animal groups on the move
Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005) · Nature · doi:10.1038/nature03236 · Cited by 2,753
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Toni, T., Welch, D., Strelkowa, N., Ipsen, A., & Stumpf, M. P. H. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0172 · Cited by 1,498
Approximate Bayesian Computation (ABC) in practice
Csilléry, K., Blum, M. G. B., Gaggiotti, O. E., & François, O. (2010) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2010.04.001 · Cited by 1,302 · cited 7×
“Those include methods that explicitly approximate p(D|/) such as Approximate Bayesian Computing (ABC) (Beaumont 2010; Csilléry et al. 2010), simulated (synthetic) pseudo-likelihoods (Hyrien et al. 2005; Wood 2010) or indirect inference (Gourieroux et al. 1993), and also other methods that allow…”
MethodInfluentialMarkov chain Monte Carlo without likelihoods
Marjoram, P., Molitor, J., Plagnol, V., & Tavaré, S. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0306899100 · Cited by 1,199
In All Likelihood: Statistical Modelling and Inference Using Likelihood
Pawitan, Y. (2001) · doi:10.1093/oso/9780198507659.001.0001 · Cited by 874
Adaptive approximate Bayesian computation
Beaumont, M. A., Cornuet, J.-M., Marin, J.-M., & Robert, C. P. (2009) · Biometrika · doi:10.1093/biomet/asp052 · Cited by 614 · cited 1×
“Particularly for the ABC approximation (Box 2), SMC algorithms may exhibit advantages over MCMCs, because they are less prone to get stuck in areas of low likelihood (Sisson et al. 2007; Beaumont et al. 2009; Toni et al. 2009, see Alg.”
MethodSimulation-based model selection for dynamical systems in systems and population biology
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Thornton, K., & Andolfatto, P. (2006) · Genetics · doi:10.1534/genetics.105.048223 · Cited by 256
Bayesian Computation and Model Selection Without Likelihoods
Leuenberger, C., & Wegmann, D. (2010) · Genetics · doi:10.1534/genetics.109.109058 · Cited by 175
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Users Polarization on Facebook and Youtube
Bessi, A., Zollo, F., Del Vicario, M., Puliga, M., Scala, A., Caldarelli, G., Uzzi, B., & Quattrociocchi, W. (2016) · PLOS ONE · doi:10.1371/journal.pone.0159641 · Cited by 285
The Generative Adversarial Brain
Gershman, S. J. (2019) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2019.00018 · Cited by 89
Hauenstein, S., Jassoy, N., Mupepele, A., Carroll, T., Kshatriya, M., Beale, C. M., & Dormann, C. F. (2022) · Mammal Review · doi:10.1111/mam.12291 · Cited by 8
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“By providing a framework for parameter estimation, model selection, and uncertainty estimation in complex simulation models, they have allowed researchers to tackle a number of problems that previously remained out of reach (Hartig et al. 2011).”
Method
Metadata
- Authors
- Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T., & Huth, A.
- Venue
- Ecology Letters
- Publisher
- Wiley
- Published
- 2011-06-17
- Volume
- 14
- Issue
- 8
- Pages
- 816-827
- ISSN
- 1461-023X (ISSN-L)
- OpenAlex
- W2136133754
- Cited by
- 384
- References
- 10
- FWCI
- 18.64
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T., & Huth, A. (2011). Statistical inference for stochastic simulation models - theory and application. Ecology Letters. https://doi.org/10.1111/j.1461-0248.2011.01640.x
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