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• W2969795864 startingPage "117" @default.
• W2969795864 abstract "Inferring hidden structure from noisy observations is a problem addressed by Bayesian statistical learning, which aims to identify optimal models of the process that generated the observations given assumptions that constrain the space of potential solutions. Animals and machines face similar model-selection problems to infer latent properties and predict future states of the world. Here we review recent attempts to explain how intelligent agents address these challenges and how their solutions relate to Bayesian principles. We focus on how constraints on available information and resources affect inference and propose a general framework that uses benefit(accuracy) and accuracy(cost) curves to assess optimality under these constraints." @default.
• W2969795864 created "2019-08-29" @default.
• W2969795864 creator A5029363341 @default.
• W2969795864 creator A5064531912 @default.
• W2969795864 creator A5087496784 @default.
• W2969795864 date "2019-10-01" @default.
• W2969795864 modified "2023-09-25" @default.
• W2969795864 title "What is optimal in optimal inference?" @default.
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• W2969795864 doi "https://doi.org/10.1016/j.cobeha.2019.07.008" @default.
• W2969795864 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7441787" @default.
• W2969795864 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32832585" @default.
• W2969795864 hasPublicationYear "2019" @default.
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