Bayesian parameter inference for individual-based models using a Particle Markov Chain Monte Carlo method

Albert, 2014, A simulated annealing approach to approximate Bayes computations, Stat. Comput., 1 Andrieu, 2010, Particle Markov chain Monte Carlo methods, J. Roy. Stat. Soc. B, 72, 269, 10.1111/j.1467-9868.2009.00736.x Andrieu, 2009, The pseudo-marginal approach for efficient Monte Carlo computations, Ann. Statistics, 37, 697, 10.1214/07-AOS574 Beaumont, 2003, Estimation of population growth or decline in genetically monitored populations, Genetics, 164, 1139, 10.1093/genetics/164.3.1139 Beaumont, 2010, Approximate bayesian computation in evolution and ecology, vol. 41, 379 Brown, 2004, Agent-based and analytical modeling to evaluate the effectiveness of greenbelts, Environ. Modell. Softw., 19, 1097, 10.1016/j.envsoft.2003.11.012 Brown, 2004, Toward a metabolic theory of ecology, Ecology, 85, 1771, 10.1890/03-9000 Blum, 2013, A comparative review of dimension reduction methods in approximate bayesian computation, Stat. Sci., 28, 189, 10.1214/12-STS406 Chopin, 2013, SMC2: an efficient algorithm for sequential analysis of state space models, J. Roy. Stat. Soc. B, 75, 397, 10.1111/j.1467-9868.2012.01046.x Csillery, 2010, Approximate bayesian computation (ABC) in practice, Trends Ecol. Evol., 25, 410, 10.1016/j.tree.2010.04.001 DeAngelis, 2005, Individual-based modeling of ecological and evolutionary processes, Annu. Rev. Ecol. Evol. S, 147, 10.1146/annurev.ecolsys.36.102003.152644 Elliot, 1971 Ellison, 2004, Bayesian inference in ecology, Ecol. Lett., 7, 509, 10.1111/j.1461-0248.2004.00603.x Fearnhead, 2012, Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation, J. Roy. Stat. Soc. B, 74, 419, 10.1111/j.1467-9868.2011.01010.x Flury, 2011, Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models, Econ. Theor., 27, 933, 10.1017/S0266466610000599 Foley, 2015, A bayesian approach to social structure uncovers cryptic regulation of group dynamics in Drosophila melanogaster, Am. Nat., 185, 797, 10.1086/681084 Gamerman, 2006 Gelman, 2014 Golightly, 2015, Delayed acceptance particle MCMC for exact inference in stochastic kinetic models, Stat. Comput., 25, 1039, 10.1007/s11222-014-9469-x Grimm, 1999, Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future?, Ecol. Model., 115, 129, 10.1016/S0304-3800(98)00188-4 Grimm, 2014, Towards better modelling and decision support: documenting model development, testing, and analysis using TRACE, Ecol. Model., 280, 129, 10.1016/j.ecolmodel.2014.01.018 Grimm, 2006, A standard protocol for describing individual-based and agent-based models, Ecol. Model., 198, 115, 10.1016/j.ecolmodel.2006.04.023 Grimm, 2010, The ODD protocol A review and first update, Ecol. Model., 221, 2760, 10.1016/j.ecolmodel.2010.08.019 Grimm, 2005 Grimm, 2005, Pattern-oriented modeling of agent-based complex systems: lessons from ecology, Science, 310, 987, 10.1126/science.1116681 Hartig, 2011, Statistical inference for stochastic simulation models - theory and application, Ecol. Lett., 14, 816, 10.1111/j.1461-0248.2011.01640.x Hartig, 2014, Technical Note: approximate Bayesian parameterization of a process-based tropical forest model, Biogeosciences, 11, 1261, 10.5194/bg-11-1261-2014 Hastings, 1970, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97, 10.1093/biomet/57.1.97 Huston, 1988, New computer-models unify ecological theory - computer-simulations show that many ecological patterns can be explained by interactions among individual organisms, Bioscience, 38, 682, 10.2307/1310870 Kantas, 2015, On particle methods for parameter estimation in state-space models, Stat. Sci., 30, 328, 10.1214/14-STS511 Kattwinkel, 2016, Modelling macroinvertebrate community dynamics in stream mesocosms contaminated with pesticide, Environ. Sci. Technol., 50, 3165, 10.1021/acs.est.5b04068 Lagarrigues, 2015, Approximate Bayesian computation to recalibrate individual-based models with population data: Illustration with a forest simulation model, Ecol. Model., 306, 278, 10.1016/j.ecolmodel.2014.09.023 Marchand, 2015, Testing models of bee foraging behavior through the analysis of pollen loads and floral density data, Ecol. Model., 313, 41, 10.1016/j.ecolmodel.2015.06.019 Marjoram, 2003, Markov chain Monte Carlo without likelihoods, P. Nat. Acad. Sci. U. S. A., 100, 15324, 10.1073/pnas.0306899100 Metropolis, 1953, Equation of state calculations by fast computing machines, J. Chem. Phys., 21, 1087, 10.1063/1.1699114 Owen, 2015, Likelihood free inference for Markov processes: a comparison, Stat. Appl. Genet. Mo. B, 14, 189 Plummer, 2006 R Core Team, 2015 Refsgaard, 2007, Uncertainty in the environmental modelling process - a framework and guidance, Environ. Modell. Softw., 22, 1543, 10.1016/j.envsoft.2007.02.004 Reichert, 2015, The conceptual foundation of environmental decision support, J. Environ. Manage, 154, 316 Robert, 2011, Lack of confidence in approximate Bayesian computation model choice, P. Nat. Acad. Sci. U. S. A., 108, 15112, 10.1073/pnas.1102900108 Schuwirth, 2015, The importance of biotic interactions for the prediction of macroinvertebrate communities under multiple stressors, Funct. Ecol., 30, 974, 10.1111/1365-2435.12605 Schuwirth, 2013, Bridging the gap between theoretical ecology and real ecosystems: modeling invertebrate community composition in streams, Ecology, 94, 368, 10.1890/12-0591.1 Sherlock, 2015, On the efficiency of pseudo-marginal random walk Metropolis algorithms, Ann. Statistics, 43, 238, 10.1214/14-AOS1278 Sibly, 2013, Representing the acquisition and use of energy by individuals in agent-based models of animal populations, Methods Ecol. Evol., 4, 151, 10.1111/2041-210x.12002 Thiele, 2014, Facilitating parameter estimation and sensitivity analysis of agent-based models: a cookbook using NetLogo and 'R', JASSS, 17, 11, 10.18564/jasss.2503 Topping, 2012, Post-hoc pattern-oriented testing and tuning of an existing large model: lessons from the field vole, PLoS ONE, 7, 10.1371/journal.pone.0045872 van der Vaart, 2015, Calibration and evaluation of individual-based models using Approximate Bayesian Computation, Ecol. Model., 312, 182, 10.1016/j.ecolmodel.2015.05.020