Approximate Bayesian Computation to Handle Aggregated Insurance Data

Approximate Bayesian Computation to Handle Aggregated Insurance Data

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Approximate Bayesian Computation (ABC) is a statistical learning technique to select and calibrate models in an automated fashion using the data at hand. It consists in simulating synthetic data from the potential models and assessing the distance between the synthetic and observed data. The method is easy to explain and to implement which makes it very appealing from a practitioner point of view. An application to a real world insurance dataset is presented.

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