Bayesian models of perception and action: an introduction

By:

Ma, Wei Ji

Contributor(s):

Material type: Text

TextPublication details: MIT Press Cambridge 2023Description: xiii, 382 pISBN:

9780262047593

Subject(s):

Bayesian statistical decision theory

DDC classification:

519.542 MA

Summary: An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action. Many forms of perception and action can be mathematically modeled as probabilistic—or Bayesian—inference, a method used to draw conclusions from uncertain evidence. According to these models, the human mind behaves like a capable data scientist or crime scene investigator when dealing with noisy and ambiguous data. This textbook provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action. Featuring extensive examples and illustrations, Bayesian Models of Perception and Action is the first textbook to teach this widely used computational framework to beginners. • Introduces Bayesian models of perception and action, which are central to cognitive science and neuroscience • Beginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex concepts • Broad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematics • Written by leaders in the field of computational approaches to mind and brain (https://mitpress.mit.edu/9780262047593/bayesian-models-of-perception-and-action/)

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.542 MA (Browse shelf(Opens below))	1	Available		007044

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Previous	519.542 GAR Bayesian optimization	519.542 LIO Bayesian inference and computation in reliability and survival analysis	519.542 LON Statistics for making decisions	519.542 MA Bayesian models of perception and action: an introduction	519.542 WAT Mathematical theory of bayesian statistics	519.544 FRO Hypothesis testing: an intuitive guide for making data driven decisions	519.55 HUA Applied time series analysis and forecasting with python	Next

An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action.

Many forms of perception and action can be mathematically modeled as probabilistic—or Bayesian—inference, a method used to draw conclusions from uncertain evidence. According to these models, the human mind behaves like a capable data scientist or crime scene investigator when dealing with noisy and ambiguous data. This textbook provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action. Featuring extensive examples and illustrations, Bayesian Models of Perception and Action is the first textbook to teach this widely used computational framework to beginners.

• Introduces Bayesian models of perception and action, which are central to cognitive science and neuroscience
• Beginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex concepts
• Broad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematics
• Written by leaders in the field of computational approaches to mind and brain
(https://mitpress.mit.edu/9780262047593/bayesian-models-of-perception-and-action/)

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