Bayesian Filter Design for Computational Medicine
This book serves as a tutorial that explains how different state estimators (Bayesian filters) can be built when all or part of the observations are binary. The book begins by briefly motivating the need for point process state estimation followed by an introduction to the overall approach, as well...
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| Formato: | Libro |
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Springer International Publishing
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=3866589&site=ehost-live header: @attributes: shortDbName: nlebk uiTerm: 3866589 longDbName: eBook Collection (EBSCOhost) uiTag: AN controlInfo: bkinfo: btl: Bayesian Filter Design for Computational Medicine aug: au: Dilranjan S. Wickramasuriya Rose T. Faghih isbn: 9783031471032 9783031471049 imageinfo: pubinfo: dt: @attributes: year: 2024 month: 01 day: 01 dtAvail: @attributes: year: 2024 month: 04 day: 12 pub: Springer International Publishing pubContract: Springer Nature place: Cham price: 0.01 limitsGroup: maxCheckoutDays: 1500 copyPages: 10 pda: N printPagesOffline: 10 printPagesOnline: 10 previewPages: 0 prePubGroup: dewey: @attributes: class: 610.21 item: 610 .21 lc: @attributes: class: RA409 item: RA 409 artinfo: ui: 3866589 1428780971 formats: fmt: – @attributes: type: EB doid: NL$3866589$PDF caption: PDF download: Y – @attributes: type: EK doid: NL$3866589$EPUB caption: EPUB download: Y tig: atl: Bayesian Filter Design for Computational Medicine ptl: Bayesian Filter Design for Computational Medicine aug: au: Dilranjan S. Wickramasuriya Rose T. Faghih su: Bayesian statistical decision theory Medical statistics sug: subj: SCIENCE / Life Sciences / Neuroscience SCIENCE / Life Sciences / Biophysics TECHNOLOGY & ENGINEERING / Biomedical TECHNOLOGY & ENGINEERING / Signals & Signal Processing Bayesian statistical decision theory Medical statistics ab: This book serves as a tutorial that explains how different state estimators (Bayesian filters) can be built when all or part of the observations are binary. The book begins by briefly motivating the need for point process state estimation followed by an introduction to the overall approach, as well as some basic background material in statistics that are necessary for the equation derivations that are utilized in subsequent chapters. The subsequent chapters focus on different state-space models and provide step-by-step explanations on how to build the corresponding Bayesian filters. Each of the main chapters that describes a single state-space model also describes the corresponding MATLAB code examples at the end. Descriptions are also provided regarding the code. The code contains both simulated and experimental data examples. All the experimental data examples are taken from real-world experiments. The experiments involve the recording of skin conductance, heartrate and blood cortisol data. A MATLAB toolbox of code examples that cover the different filters covered in the book is included in a companion webpage. The book is primarily intended for graduate students in either electrical engineering or biomedical engineering who will be beginning research in state estimation related to point process data or mixed data (i.e., point processes and other types of observations). The book can also be used by practicing researchers who measure skin conductance and heart rate or pulsatile hormones in their own work (e.g. in psychology). This is an open access book. pubtype: eBook doctype: Book ougenre: Book language: English copyright: @attributes: flag: N copyrightText: holdings: @attributes: islocal: N |
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