Multi Level Bayesian Models For Environment Perception
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Author | : Csaba Benedek |
Publisher | : Springer Nature |
Total Pages | : 208 |
Release | : 2022-04-18 |
Genre | : Mathematics |
ISBN | : 3030836541 |
This book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed static or dynamic environment. To ensure a sound theoretical basis of the new models, the surveys and algorithmic developments are performed in well-established Bayesian frameworks. Low level scene understanding functions are formulated as various image segmentation problems, where the advantages of probabilistic inference techniques such as Markov Random Fields (MRF) or Mixed Markov Models are considered. For the object level scene analysis, the book mainly relies on the literature of Marked Point Process (MPP) approaches, which consider strong geometric and prior interaction constraints in object population modeling. In particular, key developments are introduced in the spatial hierarchical decomposition of the observed scenarios, and in the temporal extension of complex MRF and MPP models. Apart from utilizing conventional optical sensors, case studies are provided on passive radar (ISAR) and Lidar-based Bayesian environment perception tasks. It is shown, via several experiments, that the proposed contributions embedded into a strict mathematical toolkit can significantly improve the results in real world 2D/3D test images and videos, for applications in video surveillance, smart city monitoring, autonomous driving, remote sensing, and optical industrial inspection.
Author | : Eric Parent |
Publisher | : CRC Press |
Total Pages | : 429 |
Release | : 2012-08-21 |
Genre | : Mathematics |
ISBN | : 1584889195 |
Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.
Author | : Peter D. Congdon |
Publisher | : CRC Press |
Total Pages | : 506 |
Release | : 2019-09-16 |
Genre | : Mathematics |
ISBN | : 0429532903 |
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website
Author | : Wei Ji Ma |
Publisher | : MIT Press |
Total Pages | : 409 |
Release | : 2023-08-08 |
Genre | : Science |
ISBN | : 0262047594 |
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
Author | : Mevin B. Hooten |
Publisher | : CRC Press |
Total Pages | : 591 |
Release | : 2019-05-15 |
Genre | : Science |
ISBN | : 0429513372 |
Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models. Features: R code implementing algorithms to fit Bayesian models using real and simulated data examples. A comprehensive review of statistical models commonly used in ecological and environmental science. Overview of Bayesian computational methods such as importance sampling, MCMC, and HMC. Derivations of the necessary components to construct statistical algorithms from scratch. Bringing Bayesian Models to Life contains a comprehensive treatment of models and associated algorithms for fitting the models to data. We provide detailed and annotated R code in each chapter and apply it to fit each model we present to either real or simulated data for instructional purposes. Our code shows how to create every result and figure in the book so that readers can use and modify it for their own analyses. We provide all code and data in an organized set of directories available at the authors' websites.
Author | : Elaine Chew |
Publisher | : World Scientific |
Total Pages | : 315 |
Release | : 2016-07-21 |
Genre | : Mathematics |
ISBN | : 9813140119 |
Mathemusical Conversations celebrates the understanding of music through mathematics, and the appreciation of mathematics through music. This volume is a compilation of the invited talks given at the Mathemusical Conversations workshop that took place in Singapore from 13-15 February 2015, organized by Elaine Chew in partnership with Gérard Assayag for the scientific program and with Bernard Lanskey for the artistic program. The contributors are world experts and leading scholars, writing on the intersection of music and mathematics. They also focus on performance and composition, two topics which are foundational both to the understanding of human creativity and to the creation of tomorrow's music technologies. This book is essential reading for researchers in both music and mathematics. It will also appeal more broadly to scholars, students, musicians, and anyone interested in new perspectives on the intimate relationship between these two universal human activities.
Author | : Juan Pimentel |
Publisher | : SAE International |
Total Pages | : 222 |
Release | : 2019-03-07 |
Genre | : Technology & Engineering |
ISBN | : 0768002826 |
Safety has been ranked as the number one concern for the acceptance and adoption of automated vehicles since safety has driven some of the most complex requirements in the development of self-driving vehicles. Recent fatal accidents involving self-driving vehicles have uncovered issues in the way some automated vehicle companies approach the design, testing, verification, and validation of their products. Traditionally, automotive safety follows functional safety concepts as detailed in the standard ISO 26262. However, automated driving safety goes beyond this standard and includes other safety concepts such as safety of the intended functionality (SOTIF) and multi-agent safety. The Safety of Controllers, Sensors, and Actuators addresses the concept of safety for self-driving vehicles through the inclusion of 10 recent and highly relevent SAE technical papers. Topics that these papers feature include risk reduction techniques in semiconductor-based systems, component certification, and safety assessment and audits for vehcicle components. As the fifth title in a series on automated vehicle safety, this contains introductory content by the Editor with 10 SAE technical papers specifically chosen to illuminate the specific safety topic of that book.
Author | : Nick Heather |
Publisher | : Routledge |
Total Pages | : 725 |
Release | : 2022-03-07 |
Genre | : Psychology |
ISBN | : 1000540065 |
This ground-breaking book advances the fundamental debate about the nature of addiction. As well as presenting the case for seeing addiction as a brain disease, it brings together all the most cogent and penetrating critiques of the brain disease model of addiction (BDMA) and the main grounds for being skeptical of BDMA claims. The idea that addiction is a brain disease dominates thinking and practice worldwide. However, the editors of this book argue that our understanding of addiction is undergoing a revolutionary change, from being considered a brain disease to a disorder of voluntary behavior. The resolution of this controversy will determine the future of scientific progress in understanding addiction, together with necessary advances in treatment, prevention, and societal responses to addictive disorders. This volume brings together the various strands of the contemporary debate about whether or not addiction is best regarded as a brain disease. Contributors offer arguments for and against, and reasons for uncertainty; they also propose novel alternatives to both brain disease and moral models of addiction. In addition to reprints of classic articles from the addiction research literature, each section contains original chapters written by authorities on their chosen topic. The editors have assembled a stellar cast of chapter authors from a wide range of disciplines – neuroscience, philosophy, psychiatry, psychology, cognitive science, sociology, and law – including some of the most brilliant and influential voices in the field of addiction studies today. The result is a landmark volume in the study of addiction which will be essential reading for advanced students and researchers in addiction as well as professionals such as medical practitioners, psychiatrists, psychologists of all varieties, and social workers.
Author | : Juan Pimentel |
Publisher | : SAE International |
Total Pages | : 210 |
Release | : 2019-03-07 |
Genre | : Technology & Engineering |
ISBN | : 0768002354 |
Safety has been ranked as the number one concern for the acceptance and adoption of automated vehicles since safety has driven some of the most complex requirements in the development of self-driving vehicles. Recent fatal accidents involving self-driving vehicles have uncovered issues in the way some automated vehicle companies approach the design, testing, verification, and validation of their products. Traditionally, automotive safety follows functional safety concepts as detailed in the standard ISO 26262. However, automated driving safety goes beyond this standard and includes other safety concepts such as safety of the intended functionality (SOTIF) and multi-agent safety. Safety of the Intended Functionality (SOTIF) addresses the concept of safety for self-driving vehicles through the inclusion of 10 recent and highly relevent SAE technical papers. Topics that these papers feature include the system engineering management approach and redundancy technical approach to safety. As the third title in a series on automated vehicle safety, this contains introductory content by the Editor with 10 SAE technical papers specifically chosen to illuminate the specific safety topic of that book.
Author | : Mei-Po Kwan |
Publisher | : MDPI |
Total Pages | : 382 |
Release | : 2019-07-12 |
Genre | : Medical |
ISBN | : 3039211838 |
Environmental health researchers have long used concepts like the neighborhood effect to assessing people’s exposure to environmental influences and the associated health impact. However, these are static notions that ignore people’s daily mobility at various spatial and temporal scales (e.g., daily travel, migratory movements, and movements over the life course) and the influence of neighborhood contexts outside their residential neighborhoods. Recent studies have started to incorporate human mobility, non-residential neighborhoods, and the temporality of exposures through collecting and using data from GPS, accelerometers, mobile phones, various types of sensors, and social media. Innovative approaches and methods have been developed. This Special Issue aims to showcase studies that use new approaches, methods, and data to examine the role of human mobility and non-residential contexts on human health behaviors and outcomes. It includes 21 articles that cover a wide range of topics, including individual exposure to air pollution, exposure and access to green spaces, spatial access to healthcare services, environmental influences on physical activity, food environmental and diet behavior, exposure to noise and its impact on mental health, and broader methodological issues such as the uncertain geographic context problem (UGCoP) and the neighborhood effect averaging problem (NEAP). This collection will be a valuable reference for scholars and students interested in recent advances in the concepts and methods in environmental health and health geography.