The Experts below are selected from a list of 201678 Experts worldwide ranked by ideXlab platform
V Dose - One of the best experts on this subject based on the ideXlab platform.
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bayesian Probability Theory applications in the physical sciences
2014Co-Authors: Wolfgang Von Der Linden, V Dose, Udo Von ToussaintAbstract:From the basics to the forefront of modern research, this book presents all aspects of Probability Theory, statistics and data analysis from a Bayesian perspective for physicists and engineers. The book presents the roots, applications and numerical implementation of Probability Theory, and covers advanced topics such as maximum entropy distributions, stochastic processes, parameter estimation, model selection, hypothesis testing and experimental design. In addition, it explores state-of-the art numerical techniques required to solve demanding real-world problems. The book is ideal for students and researchers in physical sciences and engineering.
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background source separation in astronomical images with bayesian Probability Theory i the method
Monthly Notices of the Royal Astronomical Society, 2009Co-Authors: F Guglielmetti, R. Fischer, V DoseAbstract:A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian Probability Theory is applied to gain insight into the co-existence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. A multiresolution analysis is used for revealing faint and extended objects in the frame of the Bayesian mixture model. All the revealed sources are parametrized automatically providing source position, net counts, morphological parameters and their errors. We demonstrate the capability of our method by applying it to three simulated data sets characterized by different background and source intensities. The results of employing two different prior knowledge on the source signal distribution are shown. The probabilistic method allows for the detection of bright and faint sources independently of their morphology and the kind of background. The results from our analysis of the three simulated data sets are compared with other source detection methods. Additionally, the technique is applied to ROSAT All-Sky Survey data.
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background source separation in astronomical images with bayesian Probability Theory i the method
arXiv: Instrumentation and Methods for Astrophysics, 2009Co-Authors: F Guglielmetti, R. Fischer, V DoseAbstract:A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian Probability Theory is applied to gain insight into the coexistence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. A multi-resolution analysis is used for revealing faint and extended objects in the frame of the Bayesian mixture model. All the revealed sources are parameterized automatically providing source position, net counts, morphological parameters and their errors.
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Thomson scattering analysis with the Bayesian Probability Theory
Plasma Physics and Controlled Fusion, 2002Co-Authors: Rainer Fischer, A Dinklage, C. Wendland, S. Gori, V DoseAbstract:Electron density and electron temperature profiles are reconstructed from Thomson scattering data on the stellarator Wendelstein 7-AS by means of systematic statistical modelling employing the Bayesian Probability Theory (BPT). The BPT allows for systematic combination of all information entering the measurement descriptive model considering all uncertainties of the measured data, calibration measurements, physical model parameters and measurement nuisance parameters. The BPT results are consistent with the ratio-evaluation method (REM) which is used to determine the electron temperature from the ratios of scattering signals. If compared to the sequential REM, the Bayesian error analysis is much more informative because it yields Probability density functions of the quantities of interest. Moreover, systematic consideration of all the obtainable raw data, in particular those data suffering from low signal levels, results in an improved evaluation for weakly informative data. Sensitivity analysis of model parameters allows for finding crucial uncertainties which has impact on both diagnostic improvement and design.
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decomposition of multicomponent mass spectra using bayesian Probability Theory
Journal of Mass Spectrometry, 2002Co-Authors: H D Kang, R Preuss, T Schwarzselinger, V DoseAbstract:We present a method for the decomposition of the mass spectra of mixed gases using Bayesian Probability Theory. The method works without any calibration measurement and therefore applies also to the analysis of spectra containing unstable species. For the example of mixtures of three different hydrocarbon gases the algorithm provides concentrations and cracking coefficients of each mixture component and also their confidence intervals. The amount of information needed to obtain reliable results and its relation to the accuracy of our analysis are discussed. Copyright © 2002 John Wiley & Sons, Ltd.
Afshin Shoeibi - One of the best experts on this subject based on the ideXlab platform.
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handling of uncertainty in medical data using machine learning and Probability Theory techniques a review of 30 years 1991 2020
Annals of Operations Research, 2021Co-Authors: Roohallah Alizadehsani, Mohamad Roshanzamir, Sadiq Hussain, Abbas Khosravi, Afsaneh Koohestani, Mohammad Hossein Zangooei, Moloud Abdar, Adham Beykikhoshk, Afshin ShoeibiAbstract:Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and Probability Theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using Probability Theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.
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handling of uncertainty in medical data using machine learning and Probability Theory techniques a review of 30 years 1991 2020
arXiv: Artificial Intelligence, 2020Co-Authors: Roohallah Alizadehsani, Mohamad Roshanzamir, Sadiq Hussain, Abbas Khosravi, Afsaneh Koohestani, Mohammad Hossein Zangooei, Moloud Abdar, Adham Beykikhoshk, Afshin ShoeibiAbstract:Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and Probability Theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using Probability Theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased.
Chengkuo Lee - One of the best experts on this subject based on the ideXlab platform.
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unveiling stimulation secrets of electrical excitation of neural tissue using a circuit Probability Theory
Frontiers in Computational Neuroscience, 2020Co-Authors: Hao Wang, Jiahui Wang, Xin Yuan Thow, Sanghoon Lee, Wendy Yen Xian Peh, Nitish V Thakor, Chengkuo LeeAbstract:Electrical excitation of neural tissue has wide applications, but how electrical stimulation interacts with neural tissue remains to be elucidated. Here, we propose a new Theory, named the Circuit-Probability Theory, to reveal how this physical interaction happen. The relation between the electrical stimulation input and the neural response can be theoretically calculated. We show that many empirical models, including strength-duration relationship and linear-non-linear-Poisson model, can be theoretically explained, derived, and amended using our Theory. Furthermore, this Theory can explain the complex non-linear and resonant phenomena and fit in vivo experiment data. In this letter, we validated an entirely new framework to study electrical stimulation on neural tissue, which is to simulate voltage waveforms using a parallel RLC circuit first, and then calculate the excitation Probability stochastically.
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unveiling stimulation secrets of electrical excitation of neural tissue using a circuit Probability Theory
arXiv: Neurons and Cognition, 2018Co-Authors: Hao Wang, Jiahui Wang, Xin Yuan Thow, Sanghoon Lee, Wendy Yen Xian Peh, Nitish V Thakor, Chengkuo LeeAbstract:A new Theory, named the Circuit-Probability Theory, is proposed to unveil the secret of electrical nerve stimulation, essentially explain the nonlinear and resonant phenomena observed when neural and non-neural tissues are electrically stimulated. For the explanation of frequency dependent response, an inductor is involved in the neural circuit model. Furthermore, predicted response to varied stimulation strength is calculated stochastically. Based on this Theory, many empirical models, such as strength-duration relationship and LNP model, can be theoretically explained, derived, and amended. This Theory can explain the complex nonlinear interactions in electrical stimulation and fit in vivo experiment data on stimulation-responses of many experiments. As such, the C-P Theory should be able to guide novel experiments and more importantly, offer an in-depth physical understanding of the neural tissue. As a promising neural model, we can even further explore the more accurate circuit configuration and Probability equation to better describe the electrical stimulation of neural tissues in the future.
Hao Wang - One of the best experts on this subject based on the ideXlab platform.
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unveiling stimulation secrets of electrical excitation of neural tissue using a circuit Probability Theory
Frontiers in Computational Neuroscience, 2020Co-Authors: Hao Wang, Jiahui Wang, Xin Yuan Thow, Sanghoon Lee, Wendy Yen Xian Peh, Nitish V Thakor, Chengkuo LeeAbstract:Electrical excitation of neural tissue has wide applications, but how electrical stimulation interacts with neural tissue remains to be elucidated. Here, we propose a new Theory, named the Circuit-Probability Theory, to reveal how this physical interaction happen. The relation between the electrical stimulation input and the neural response can be theoretically calculated. We show that many empirical models, including strength-duration relationship and linear-non-linear-Poisson model, can be theoretically explained, derived, and amended using our Theory. Furthermore, this Theory can explain the complex non-linear and resonant phenomena and fit in vivo experiment data. In this letter, we validated an entirely new framework to study electrical stimulation on neural tissue, which is to simulate voltage waveforms using a parallel RLC circuit first, and then calculate the excitation Probability stochastically.
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unveiling stimulation secrets of electrical excitation of neural tissue using a circuit Probability Theory
arXiv: Neurons and Cognition, 2018Co-Authors: Hao Wang, Jiahui Wang, Xin Yuan Thow, Sanghoon Lee, Wendy Yen Xian Peh, Nitish V Thakor, Chengkuo LeeAbstract:A new Theory, named the Circuit-Probability Theory, is proposed to unveil the secret of electrical nerve stimulation, essentially explain the nonlinear and resonant phenomena observed when neural and non-neural tissues are electrically stimulated. For the explanation of frequency dependent response, an inductor is involved in the neural circuit model. Furthermore, predicted response to varied stimulation strength is calculated stochastically. Based on this Theory, many empirical models, such as strength-duration relationship and LNP model, can be theoretically explained, derived, and amended. This Theory can explain the complex nonlinear interactions in electrical stimulation and fit in vivo experiment data on stimulation-responses of many experiments. As such, the C-P Theory should be able to guide novel experiments and more importantly, offer an in-depth physical understanding of the neural tissue. As a promising neural model, we can even further explore the more accurate circuit configuration and Probability equation to better describe the electrical stimulation of neural tissues in the future.
Dan Voiculescu - One of the best experts on this subject based on the ideXlab platform.
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the analogues of entropy and of fisher s information measure in free Probability Theory ii
Inventiones Mathematicae, 1994Co-Authors: Dan VoiculescuAbstract:Analogues of the entropy and Fisher information measure for random variables in the context of free Probability Theory are introduced. Monotonicity properties and an analogue of the Cramer-Rao inequality are proved.
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the analogues of entropy and of fisher s information measure in free Probability Theory i
Communications in Mathematical Physics, 1993Co-Authors: Dan VoiculescuAbstract:Analogues of the entropy and Fisher information measure for random variables in the context of free Probability Theory are introduced. Monotonicity properties and an analogue of the Cramer-Rao inequality are proved.
