Analysis of multicomponent mass spectra applying Bayesian probability theory

2001 ◽  
Vol 36 (8) ◽  
pp. 866-874 ◽  
Author(s):  
T. Schwarz-Selinger ◽  
R. Preuss ◽  
V. Dose ◽  
W. von der Linden
Keyword(s):  
Probability Theory ◽  
Mass Spectra ◽  
Bayesian Probability

scholarly journals Decomposition of multicomponent mass spectra using Bayesian probability theory

2002 ◽  
Vol 37 (7) ◽  
pp. 748-754 ◽  
Author(s):  
H. D. Kang ◽  
R. Preuss ◽  
T. Schwarz-Selinger ◽  
V. Dose
Keyword(s):  
Probability Theory ◽  
Mass Spectra ◽  
Bayesian Probability

scholarly journals Inverse problems in cancellous bone: Estimation of the ultrasonic properties of fast and slow waves using Bayesian probability theory

2010 ◽  
Vol 128 (5) ◽  
pp. 2940-2948 ◽  
Author(s):  
Christian C. Anderson ◽  
Adam Q. Bauer ◽  
Mark R. Holland ◽  
Michal Pakula ◽  
Pascal Laugier ◽  
...  
Keyword(s):  
Probability Theory ◽  
Inverse Problems ◽  
Cancellous Bone ◽  
Slow Waves ◽  
Bayesian Probability ◽  
Ultrasonic Properties ◽  
Fast And Slow Waves

Planning and Evaluation of Assurance Services for Sustainability Reporting: An Evidential Reasoning Approach

2013 ◽  
Vol 27 (2) ◽  
pp. 107-126 ◽  
Author(s):  
Rajendra P. Srivastava ◽  
Sunita S. Rao ◽  
Theodore J. Mock
Keyword(s):  
Probability Theory ◽  
Theoretical Model ◽  
Corporate Sustainability ◽  
Sustainability Reporting ◽  
Belief Function ◽  
Global Reporting Initiative ◽  
Bayesian Probability ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Different Levels

ABSTRACT This study develops a framework for planning, performing, and evaluating evidence obtained to assess and control the risks of providing assurance on sustainability reports. Sustainability reporting, or corporate sustainability reporting (CSR), provides stakeholders with important information on both financial and non-financial factors related to environmental, social, and economic performance. Importantly, the presented framework is developed from both a Bayesian (probability-based theory) and Belief Function (Dempster-Shafer theory) perspective. This facilitates application of the framework to cases where the assurance provider prefers to assess risk in terms of probability versus in terms of beliefs. To demonstrate the application of this framework we evaluate assertions, sub-assertions, and audit evidence relevant to CSR based on the G3 Reporting framework developed by the Global Reporting Initiative (GRI). The paper contributes to the literature in three main areas. First, it demonstrates how evidence-based reasoning can be used for engagements where different levels of assurance are provided for the assertions being audited. Second, it shows how various items of evidence at different levels may be aggregated. Third, it presents a generic theoretical model for assuring information based on belief-based assessments, which is then contrasted with a theoretical model based on probability theory. In contrasting the two approaches, we show that in cases where initial uncertainty is substantial, the use of Dempster-Shafer theory has advantages over probability theory in terms of efficiency in achieving a targeted low level of assurance.

Comparison of conventional ultrasonic phase velocity and attenuation measurements of cancellous bone to estimates obtained using Bayesian probability theory.

2010 ◽  
Vol 127 (3) ◽  
pp. 2006-2006
Author(s):  
Christian C. Anderson ◽  
Michal Pakula ◽  
Pascal Laugier ◽  
G. Larry Bretthorst ◽  
Mark R. Holland ◽  
...  
Keyword(s):  
Probability Theory ◽  
Phase Velocity ◽  
Cancellous Bone ◽  
Bayesian Probability ◽  
Phase Velocity And Attenuation

scholarly journals Bayesian inference of multi-sensors impedance cardiography for detection of aortic dissection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vahid Badeli ◽  
Sascha Ranftl ◽  
Gian Marco Melito ◽  
Alice Reinbacher-Köstinger ◽  
Wolfgang Von Der Linden ◽  
...  
Keyword(s):  
Probability Theory ◽  
Bayesian Inference ◽  
Aortic Dissection ◽  
Electrical Impedance ◽  
Impedance Cardiography ◽  
Bayesian Probability ◽  
Content Type ◽  
Non Invasive ◽  
Life Threatening ◽  
Relevant Domain

Purpose This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients. Design/methodology/approach A 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification. Findings The Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection. Originality/value This paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.

Ultrasonic characterization of bone quality based on Bayesian probability theory.

2010 ◽  
Vol 128 (4) ◽  
pp. 2363-2363
Author(s):  
Christian C. Anderson ◽  
G. Larry Bretthorst ◽  
Mark R. Holland ◽  
James G Miller
Keyword(s):  
Probability Theory ◽  
Bone Quality ◽  
Bayesian Probability ◽  
Ultrasonic Characterization
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