Statistical inference for stochastic simulation models--theory and application.

By Florian Hartig, Justin M. Calabrese, Björn Reineking, T. Wiegand, Andreas Huth. Ecology Letters, 2011

403 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9009f1515f7c078c7c089eb2d4124139dbdeed55

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Citations in the corpus

Representing the acquisition and use of energy by individuals in agent‐based models of animal populations (2013, methodology): “Methods are available for achieving this (e.g. Piou, Berger & Grimm 2009; Beaumont 2010; Csillery et al. 2010; Hartig et al. 2011); though, implementationmay not be without problems (Robert et al. 2011).”
Machine learning and deep learning—A review for ecologists (2022, methodology): “Moreover, ML can also be used to predict the parameters of complex stochastic models (Roy et al., 2022 ; Voznica et al., 2022 ), and thus act as a likelihood-free calibration method, similar to approximate Bayesian computation (Hartig et al., 2011 ).”
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE) (2018, neutral): “6] and φ ∈ [5 , 15].”
Likelihood-free MCMC with Amortized Approximate Ratio Estimators (2019, methodology): “ics and 10;000 independent tests computed using SVB [28]. Bins outside the shaded indicate a poor approximation. 6 Related work Sampling from an implicit model p(xj) to perform statistical inference [29, 30] is a common technique in science to bypass the intractability of the likelihood. Works apply”
Hierarchical Implicit Models and Likelihood-Free Variational Inference (2017, background): “But many likelihoods of interest, such as simulator-based models [20] and generative adversarial networks [18], admit high”
RangeShifter: a platform for modelling spatial eco‐evolutionary dynamics and species' responses to environmental changes (2014, methodology): “A promising avenue could be enhancing RangeShifter such that it can be used with approximate Bayesian computation methods (Beaumont 2010; Hartig et al. 2011).”
Likelihood-free MCMC with Approximate Likelihood Ratios (2019, methodology): “Sampling from an implicit model p ( x | θ ) to perform statistical inference [29, 30] is a common technique in science to bypass the intractability of the likelihood.”
Mechanistic simulation models in macroecology and biogeography: state‐of‐art and prospects (2017, background): “…in statistical computing – for example pattern-oriented modeling (Grimm et al. 2005); or Approximate Bayesian Computation, which can generate approximate likelihoods from stochastic simulations (reviewed by Hartig et al. 2011) – will help connecting complex models with such heterogenous datasets.”
Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference (2018, methodology): “More sophisticated algorithms such as MCMC and SMC (see Hartig et al. 2011, for a basic review) require modifications to deal with the issue of different number of parameters when changing between models.”
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model (2018, background): “These models are typically non-differentiable and lead to intractable likelihoods, which renders many traditional statistical inference algorithms irrelevant and motivates a new class of so-called likelihood-free inference algorithms [5].”
Likelihood-Free Inference by Ratio Estimation (2016, methodology): “For a comprehensive introduction to the ﬁeld, we refer the reader to the review papers by Beaumont (2010); Hartig et al. (2011); Marin et al. (2012); Lintusaari et al. (2017); Sisson et al. (2018).”
Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation (2017, methodology): “…population genetics Beaumont et al. (2002); Numminen et al. (2013), genomics Marttinen et al. (2015); J¨arvenp¨a¨a et al. (2017), ecology (Wood, 2010; Hartig et al., 2011) and psychology Turner and Van Zandt (2012), see e.g. Lintusaari et al. (2017) and references therein for further examples.”
Flexible statistical inference for mechanistic models of neural dynamics (2017, methodology): “Bayesian inference for likelihood-free simulator models, also known as Approximate Bayesian Computation [14, 15, 16], provides an attractive framework for overcoming these limitations: like parameter-fitting approaches in neuroscience [2, 8, 9], it is based on comparing summary features between simu”