Probabilistic Models of Cognition - 2nd Edition
This book explores the probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models. We examine how a broad range of empirical phenomena, including intuitive physics, concept learning, causal reasoning, social cognition, and language understanding, can be modeled using probabilistic programs (using the WebPPL language). This book is an open source project. We welcome content contributions (via GitHub)! The ProbMods Contibutors are: Noah D. Goodman (editor) Joshua B. Tenenbaum Daphna Buchsbaum Joshua Hartshorne Robert Hawkins Timothy J. O’Donnell Michael Henry Tessler We are grateful for crucial technical assitance from: Andreas Stuhlmüller, Tomer Ullman, John McCoy, Long Ouyang, Julius Cheng. The construction and ongoing support of this tutorial are made possible by grants from the Office of Naval Research, the James S. McDonnell Foundation, the Stanford VPOL, and the Center for Brains, Minds, and Machines (funded by NSF
This book explores the probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models. We examine how a broad range of empirical phenomena, including intuitive physics, concept learning, causal reasoning, social cognition, and language understanding, can be modeled using probabilistic programs (using the WebPPL language). Contributors This book is an open source project. We welcome content contributions (via GitHub)! The ProbMods Contibutors are: Noah D. Goodman (editor) Joshua B. Tenenbaum Daphna Buchsbaum Joshua…
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