SMI introduces an influence-parameter family of inference schemes for multi-modular Bayesian models that interpolates between full Bayesian and cut-model inference while allowing directed control of information flow and a data-driven choice of the parameter.
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Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components
SMI introduces an influence-parameter family of inference schemes for multi-modular Bayesian models that interpolates between full Bayesian and cut-model inference while allowing directed control of information flow and a data-driven choice of the parameter.