Amortized Inference in Probabilistic Reasoning

  title={Amortized Inference in Probabilistic Reasoning},
  author={Samuel J Gershman and Noah D. Goodman},
Recent studies of probabilistic reasoning have postulated general-purpose inference algorithms that can be used to answer arbitrary queries. These algorithms are memoryless, in the sense that each query is processed independently, without reuse of earlier computation. We argue that the brain operates in the setting of amortized inference, where numerous related queries must be answered (e.g., recognizing a scene from multiple viewpoints); in this setting, memoryless algorithms can be… CONTINUE READING
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