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Titlebook: Bayesian Filter Design for Computational Medicine; A State-Space Estima Dilranjan S. Wickramasuriya,Rose T. Faghih Book‘‘‘‘‘‘‘‘ 2024 The Ed

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發(fā)表于 2025-3-21 20:09:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Bayesian Filter Design for Computational Medicine
期刊簡(jiǎn)稱A State-Space Estima
影響因子2023Dilranjan S. Wickramasuriya,Rose T. Faghih
視頻videohttp://file.papertrans.cn/182/181840/181840.mp4
發(fā)行地址Provides a tutorial-style introduction on state estimation methods based on point process observations.Includes experimental data examples are taken from real-world experiments.This book is open acces
圖書封面Titlebook: Bayesian Filter Design for Computational Medicine; A State-Space Estima Dilranjan S. Wickramasuriya,Rose T. Faghih Book‘‘‘‘‘‘‘‘ 2024 The Ed
影響因子.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 e
Pindex Book‘‘‘‘‘‘‘‘ 2024
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State-Space Model with One Binary and One Continuous Observation,ary observation . and a continuous observation .. Prior to looking into the equation derivations, however, as in the previous chapter, we will again first consider a few example scenarios where the need for such a model arises.
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State-Space Model with One MPP and One Continuous Observation, Before looking at the state-space model itself and the equation derivations, we will again first consider a scenario for where the need for such a model arises. We stated earlier that the human body is comprised of multiple internal sub-systems that are networked with one another.
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https://doi.org/10.1007/978-3-031-47104-9State-space estimation; Bayesian filtering; Bayesian decoder design; Physiological decoders; Mixed filte
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Smart Innovation, Systems and TechnologiesIn this chapter, we will consider a state-space model where a single state variable . gives rise to binary observations. We will see how the state and parameter estimation equations are derived for this case. However, prior to deriving any of the equations, we will first look at two example scenarios where the need for such a model arises.
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Human Centred Intelligent SystemsAll the MATLAB code examples accompanying this book can be run directly. The examples are self-contained and do not require additional path variables being set up. The following is a partial list of the supplementary MATLAB functions that are called at various stages by the state estimators.
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