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@misc{Lenormand2012,
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    keywords = {abc, bayes, indirect, inference, smc, stochasticity},
    month = jul,
    posted-at = {2011-11-08 17:55:10},
    priority = {2},
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@article{Beaumont2010,
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@article{cherel2015,
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    author = {Chérel, Guillaume AND Cottineau, Clémentine AND Reuillon, Romain},
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    year = {2015},
    month = {09},
    volume = {10},
    url = {https://doi.org/10.1371/journal.pone.0138212},
    pages = {1-28},
    abstract = {Models of emergent phenomena are designed to provide an explanation to global-scale phenomena from local-scale processes. Model validation is commonly done by verifying that the model is able to reproduce the patterns to be explained. We argue that robust validation must not only be based on corroboration, but also on attempting to falsify the model, i.e. making sure that the model behaves soundly for any reasonable input and parameter values. We propose an open-ended evolutionary method based on Novelty Search to look for the diverse patterns a model can produce. The Pattern Space Exploration method was tested on a model of collective motion and compared to three common a priori sampling experiment designs. The method successfully discovered all known qualitatively different kinds of collective motion, and performed much better than the a priori sampling methods. The method was then applied to a case study of city system dynamics to explore the model’s predicted values of city hierarchisation and population growth. This case study showed that the method can provide insights on potential predictive scenarios as well as falsifiers of the model when the simulated dynamics are highly unrealistic.},
    number = {9},
}
@article{wilensky1998netlogo,
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@article{wilensky1999netlogo,
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@article{jahel2023future,
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}

@article{widyastuti2022assessing,
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  author={Widyastuti, Kirana and Reuillon, Romain and Chapron, Paul and Abdussalam, Wildan and Nasir, Darmae and Harrison, Mark E and Morrogh-Bernard, Helen and Imron, Muhammad Ali and Berger, Uta},
  journal={Frontiers in Ecology and Evolution},
  volume={10},
  pages={983337},
  year={2022},
  publisher={Frontiers}
}

@incollection{raimbault2021modeling,
  title={Modeling the co-evolution of cities and networks},
  author={Raimbault, Juste},
  booktitle={Handbook of cities and networks},
  pages={166--193},
  year={2021},
  publisher={Edward Elgar Publishing}
}

@article{cottineau2015modular,
  title={A modular modelling framework for hypotheses testing in the simulation of urbanisation},
  author={Cottineau, Cl{\'e}mentine and Reuillon, Romain and Chapron, Paul and Rey-Coyrehourcq, S{\'e}bastien and Pumain, Denise},
  journal={Systems},
  volume={3},
  number={4},
  pages={348--377},
  year={2015},
  publisher={MDPI}
}

@article{brasebin20183d,
  title={3D urban data to assess local urban regulation influence},
  author={Brasebin, Micka{\"e}l and Perret, Julien and Musti{\`e}re, S{\'e}bastien and Weber, Christiane},
  journal={Computers, Environment and Urban Systems},
  volume={68},
  pages={37--52},
  year={2018},
  publisher={Elsevier}
}

@article{edali2020analysis,
  title={Analysis of an individual-based influenza epidemic model using random forest metamodels and adaptive sequential sampling},
  author={Edali, Mert and Y{\"u}cel, G{\"o}nen{\c{c}}},
  journal={Systems Research and Behavioral Science},
  volume={37},
  number={6},
  pages={936--958},
  year={2020},
  publisher={Wiley Online Library}
}

@article{llorente2023marginal,
  title={Marginal likelihood computation for model selection and hypothesis testing: an extensive review},
  author={Llorente, Fernando and Martino, Luca and Delgado, David and Lopez-Santiago, Javier},
  journal={Siam Review},
  volume={65},
  number={1},
  pages={3--58},
  year={2023},
  publisher={SIAM}
}

@article{borah2019rare,
  title={Rare pattern mining: challenges and future perspectives},
  author={Borah, Anindita and Nath, Bhabesh},
  journal={Complex \& Intelligent Systems},
  volume={5},
  pages={1--23},
  year={2019},
  publisher={Springer}
}

@article{Zhou2023,
author = {Zhou, Dawei and He, Jingrui},
title = {Rare Category Analysis for Complex Data: A Review},
year = {2023},
issue_date = {May 2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {56},
number = {5},
issn = {0360-0300},
url = {https://doi.org/10.1145/3626520},
doi = {10.1145/3626520},
abstract = {Though the sheer volume of data that is collected is immense, it is the rare categories that are often the most important in many high-impact domains, ranging from financial fraud detection in online transaction networks to emerging trend detection in social networks, from spam image detection on social media platforms to rare disease diagnosis in medical decision support systems. The unique challenges of rare category analysis include (1) the highly skewed class distribution; (2) the non-separable nature of the rare categories from the majority classes; (3) data and task heterogeneity; and (4) the time-evolving property of the input data sources. This survey reviews state-of-the-art techniques used in complex rare category analysis, where the majority classes have a smooth distribution while the minority classes exhibit the compactness property in the feature space or subspace. Rare category analysis aims to identify, characterize, represent, and interpret anomalies that not only show statistical significance but also exhibit interesting patterns (e.g., compactness, high-order structures, showing in a burst). We introduce our study, define the problem setting, and describe the unique challenges of complex rare category analysis. We then present a comprehensive review of recent advances that are designed for this problem setting, from rare category exploration without any label information to rare category exposition that characterizes rare examples with a compact representation, from the representation of rare patterns in a salient embedding space to the interpretation the prediction results and providing relevant clues for the end-users’ interpretation. Finally we discuss potential challenges and shed light on the future directions for complex rare category analysis.1},
journal = {ACM Comput. Surv.},
month = {nov},
articleno = {123},
numpages = {35},
keywords = {anomaly detection, imbalanced data distribution, Rare category analysis}
}

@article{SHEIKHOLESLAMI2017109,
title = {Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models},
journal = {Environmental Modelling \& Software},
volume = {93},
pages = {109-126},
year = {2017},
issn = {1364-8152},
doi = {https://doi.org/10.1016/j.envsoft.2017.03.010},
url = {https://www.sciencedirect.com/science/article/pii/S1364815216305096},
author = {Razi Sheikholeslami and Saman Razavi},
keywords = {Design of computer experiments, Sequential sampling, Optimal Latin hypercube sampling, Monte Carlo simulation, Uncertainty analysis, Sensitivity analysis},
abstract = {Efficient sampling strategies that scale with the size of the problem, computational budget, and users’ needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional properties of interest (Latin hypercube properties, space-filling, etc.), as the sample size grows. Unlike Latin hypercube sampling, PLHS generates a series of smaller sub-sets (slices) such that (1) the first slice is Latin hypercube, (2) the progressive union of slices remains Latin hypercube and achieves maximum stratification in any one-dimensional projection, and as such (3) the entire sample set is Latin hypercube. The performance of PLHS is compared with benchmark sampling strategies across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis. Our results indicate that PLHS leads to improved efficiency, convergence, and robustness of sampling-based analyses.}
}

@article{tokdar2010importance,
  title={Importance sampling: a review},
  author={Tokdar, Surya T and Kass, Robert E},
  journal={Wiley Interdisciplinary Reviews: Computational Statistics},
  volume={2},   
  number={1},
  pages={54--60},
  year={2010},
  publisher={Wiley Online Library}
}


@article{mcinnes2017hdbscan,
  title={{hdbscan}: Hierarchical density based clustering},
  author={McInnes, Leland and Healy, John and Astels, Steve},
  journal={The Journal of Open Source Software},
  volume={2},
  number={11},
  pages={205},
  year={2017},
  doi={10.21105/joss.00205}
}

@article{dempster1977em,
  title={Maximum likelihood from incomplete data via the EM algorithm},
  author={Dempster, Arthur P and Laird, Nan M and Rubin, Donald B},
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  number={1},
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}