A framework that uses hidden Markov regimes and agent clustering to calibrate dynamic and heterogeneous parameters in agent-based models, reducing in-sample error on synthetic and real estate cases.
Journal of Machine Learning Research 12(Oct):2879–2904
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Automatic Calibration of Dynamic and Heterogeneous Parameters in Agent-based Model
A framework that uses hidden Markov regimes and agent clustering to calibrate dynamic and heterogeneous parameters in agent-based models, reducing in-sample error on synthetic and real estate cases.