Perception as Bayesian Inference

Perception as Bayesian Inference
Author: David C. Knill
Publisher: Cambridge University Press
Total Pages: 534
Release: 1996-09-13
Genre: Computers
ISBN: 9780521461092

This 1996 book describes an exciting theoretical paradigm for visual perception based on experimental and computational insights.

Probabilistic Models of the Brain

Probabilistic Models of the Brain
Author: Rajesh P.N. Rao
Publisher: MIT Press
Total Pages: 348
Release: 2002-03-29
Genre: Medical
ISBN: 9780262264327

A survey of probabilistic approaches to modeling and understanding brain function. Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function. This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.

The Neural Bases of Multisensory Processes

The Neural Bases of Multisensory Processes
Author: Micah M. Murray
Publisher: CRC Press
Total Pages: 800
Release: 2011-08-25
Genre: Science
ISBN: 1439812179

It has become accepted in the neuroscience community that perception and performance are quintessentially multisensory by nature. Using the full palette of modern brain imaging and neuroscience methods, The Neural Bases of Multisensory Processes details current understanding in the neural bases for these phenomena as studied across species, stages of development, and clinical statuses. Organized thematically into nine sub-sections, the book is a collection of contributions by leading scientists in the field. Chapters build generally from basic to applied, allowing readers to ascertain how fundamental science informs the clinical and applied sciences. Topics discussed include: Anatomy, essential for understanding the neural substrates of multisensory processing Neurophysiological bases and how multisensory stimuli can dramatically change the encoding processes for sensory information Combinatorial principles and modeling, focusing on efforts to gain a better mechanistic handle on multisensory operations and their network dynamics Development and plasticity Clinical manifestations and how perception and action are affected by altered sensory experience Attention and spatial representations The last sections of the book focus on naturalistic multisensory processes in three separate contexts: motion signals, multisensory contributions to the perception and generation of communication signals, and how the perception of flavor is generated. The text provides a solid introduction for newcomers and a strong overview of the current state of the field for experts.

The Oxford Handbook of Philosophy of Perception

The Oxford Handbook of Philosophy of Perception
Author: Mohan Matthen
Publisher:
Total Pages: 945
Release: 2015
Genre: Philosophy
ISBN: 0199600473

The Oxford Handbook of the Philosophy of Perception is a survey by leading philosophical thinkers of contemporary issues and new thinking in philosophy of perception. It includes sections on the history of the subject, introductions to contemporary issues in the epistemology, ontology and aesthetics of perception, treatments of the individual sense modalities and of the things we perceive by means of them, and a consideration of how perceptual information is integrated and consolidated. New analytic tools and applications to other areas of philosophy are discussed in depth. Each of the forty-five entries is written by a leading expert, some collaborating with younger figures; each seeks to introduce the reader to a broad range of issues. All contain new ideas on the topics covered; together they demonstrate the vigour and innovative zeal of a young field. The book is accessible to anybody who has an intellectual interest in issues concerning perception.

Bayesian Brain

Bayesian Brain
Author: Kenji Doya
Publisher: MIT Press
Total Pages: 341
Release: 2007
Genre: Bayesian statistical decision theory
ISBN: 026204238X

Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.

Bayesian Statistics for Experimental Scientists

Bayesian Statistics for Experimental Scientists
Author: Richard A. Chechile
Publisher: MIT Press
Total Pages: 473
Release: 2020-09-08
Genre: Mathematics
ISBN: 0262360705

An introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis. This book offers an introduction to the Bayesian approach to statistical inference, with a focus on nonparametric and distribution-free methods. It covers not only well-developed methods for doing Bayesian statistics but also novel tools that enable Bayesian statistical analyses for cases that previously did not have a full Bayesian solution. The book's premise is that there are fundamental problems with orthodox frequentist statistical analyses that distort the scientific process. Side-by-side comparisons of Bayesian and frequentist methods illustrate the mismatch between the needs of experimental scientists in making inferences from data and the properties of the standard tools of classical statistics.

Computational Bayesian Statistics

Computational Bayesian Statistics
Author: M. Antónia Amaral Turkman
Publisher: Cambridge University Press
Total Pages: 256
Release: 2019-02-28
Genre: Business & Economics
ISBN: 1108481035

This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods.

Bayesian inference with INLA

Bayesian inference with INLA
Author: Virgilio Gomez-Rubio
Publisher: CRC Press
Total Pages: 330
Release: 2020-02-20
Genre: Mathematics
ISBN: 1351707205

The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website. This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.

Bayesian Inference for Partially Identified Models

Bayesian Inference for Partially Identified Models
Author: Paul Gustafson
Publisher: CRC Press
Total Pages: 196
Release: 2020-06-30
Genre: Bayesian statistical decision theory
ISBN: 9780367570538

This book shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years of research in this area, the author presents a thorough overview of the statistical theory, properties, and applications of PIM

Surfing Uncertainty

Surfing Uncertainty
Author: Andy Clark
Publisher: Oxford University Press, USA
Total Pages: 425
Release: 2016
Genre: Medical
ISBN: 0190217014

Exciting new theories in neuroscience, psychology, and artificial intelligence are revealing minds like ours as predictive minds, forever trying to guess the incoming streams of sensory stimulation before they arrive. In this up-to-the-minute treatment, philosopher and cognitive scientist Andy Clark explores new ways of thinking about perception, action, and the embodied mind.