東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Bayesian filter design for computational medicine = a state-space estimation framework /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian filter design for computational medicine/ by Dilranjan S. Wickramasuriya, Rose T. Faghih.
其他題名:	a state-space estimation framework /
作者:	Wickramasuriya, Dilranjan S.
其他作者:	Faghih, Rose T.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xv, 228 p. :ill., digital ;24 cm.
內容註:	Introduction -- Some Useful Statistical Results -- State-space Model with One Binary Observation -- State-space Model with One Binary and One Continuous Observation -- State-space Model with One Binary and Two Continuous Observations -- State-space Model with One Binary, Two Continuous and a Spiking-type Observation -- State-space Model with One Marked Point Process (MPP) Observation -- Additional Models and Derivations -- MATLAB Code Examples -- List of Supplementary MATLAB Functions.
Contained By:	Springer Nature eBook
標題:	Medical statistics. -
電子資源:	https://doi.org/10.1007/978-3-031-47104-9
ISBN:	9783031471049

Bayesian filter design for computational medicine = a state-space estimation framework /
Wickramasuriya, Dilranjan S.

Bayesian filter design for computational medicinea state-space estimation framework /[electronic resource] :by Dilranjan S. Wickramasuriya, Rose T. Faghih. - Cham :Springer International Publishing :2024. - xv, 228 p. :ill., digital ;24 cm.

Introduction -- Some Useful Statistical Results -- State-space Model with One Binary Observation -- State-space Model with One Binary and One Continuous Observation -- State-space Model with One Binary and Two Continuous Observations -- State-space Model with One Binary, Two Continuous and a Spiking-type Observation -- State-space Model with One Marked Point Process (MPP) Observation -- Additional Models and Derivations -- MATLAB Code Examples -- List of Supplementary MATLAB Functions.

Open access.

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 provides 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, heart rate 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.

ISBN: 9783031471049

Standard No.: 10.1007/978-3-031-47104-9doiSubjects--Uniform Titles:

MATLAB.
Subjects--Topical Terms:

533219
Medical statistics.

LC Class. No.: RA409

Dewey Class. No.: 610.21

Bayesian filter design for computational medicine = a state-space estimation framework /
LDR:03133nmm a2200337 a 4500 001 2369493
003 DE-He213
005 20240329202130.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031471049 $q (electronic bk.)
020 $a 9783031471032 $q (paper)
024 7 $a 10.1007/978-3-031-47104-9 $2 doi
035 $a 978-3-031-47104-9
040 $a GP $c GP
041 0 $a eng
050 4 $a RA409
072 7 $a PSAN $2 bicssc
072 7 $a SCI089000 $2 bisacsh
072 7 $a PSAN $2 thema
082 0 4 $a 610.21 $2 23
090 $a RA409 $b .W637 2024
100 1 $a Wickramasuriya, Dilranjan S. $3 3350855
245 1 0 $a Bayesian filter design for computational medicine $h [electronic resource] : $b a state-space estimation framework / $c by Dilranjan S. Wickramasuriya, Rose T. Faghih.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xv, 228 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Some Useful Statistical Results -- State-space Model with One Binary Observation -- State-space Model with One Binary and One Continuous Observation -- State-space Model with One Binary and Two Continuous Observations -- State-space Model with One Binary, Two Continuous and a Spiking-type Observation -- State-space Model with One Marked Point Process (MPP) Observation -- Additional Models and Derivations -- MATLAB Code Examples -- List of Supplementary MATLAB Functions.
506 $a Open access.
520 $a 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 provides 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, heart rate 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.
630 0 0 $a MATLAB. $3 532595
650 0 $a Medical statistics. $3 533219
650 0 $a Bayesian statistical decision theory. $3 551404
650 1 4 $a Computational Neuroscience. $3 3593148
650 2 4 $a Neuroengineering. $3 3596917
650 2 4 $a Biomedical Engineering and Bioengineering. $3 3381533
650 2 4 $a Digital and Analog Signal Processing. $3 3538815
650 2 4 $a Mechanobiological Cell Signaling. $3 3594304
700 1 $a Faghih, Rose T. $3 3714196
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-47104-9
950 $a Biomedical and Life Sciences (SpringerNature-11642)