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Bayesian marginalization is shown to measure the complexity of a model, objectively quantifying Ockham's Razor via the Ockham parameter inference (OPI). This is not possible in likelihood-based inference. The OPI rejects any hypothesis which is poorly supported by the data. This leads to the Censored Marginal A Posteriori (CMaAP) estimation policy which returns confident estimates well below the maximum...
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