scholarly journals A STUDY ON APPLICATION OF BAYES’ THEOREM IN APPIN TECHNOLOGY

2019 ◽  
Vol 9 (1) ◽  
pp. 01-05
Author(s):  
Durga Devi .S ◽  
Elackya .S ◽  
Abhinandana .R
Keyword(s):  
Bayes Theorem

Bayesian Applications in Auditory Research

2019 ◽  
Vol 62 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Garnett P. McMillan ◽  
John B. Cannon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Methods ◽  
Prior Distribution ◽  
Interim Analysis ◽  
Iterative Process ◽  
Bayes Theorem ◽  
Sound Therapy ◽  
The Many ◽  
Auditory Research

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592

Outcome – Studien im Zusammenhang mit Laboranalysen

2015 ◽  
Vol 72 (2) ◽  
pp. 93-98
Author(s):  
Andreas R. Huber
Keyword(s):  
Bayes Theorem ◽  
Wird Eine ◽  
Psa Test

Biochemische Erkenntnisse und das Wissen über Stoffwechselvorgänge bis zum einzelnen Molekül und die rasche Entwicklung von neuen, sehr leistungsfähigen Methoden haben es erlaubt, dass die Labormedizin als wichtiger Bestandteil von Diagnose, Ausschluss, Therapie, Monitoring und als prädiktiver Test Einzug in die Medizin erhalten hat. Wichtig ist nicht nur die Qualität des Assays, sondern auch das Fachwissen um den Test, d. h. dass ein Test für die richtige Fragestellung eingesetzt wird und die Wertigkeit dem Kliniker bekannt ist. Hinzu kommen das Einhalten der präanalytischen Bedingungen, Kenntnisse über statistische Fakten wie Bayes Theorem und der gekonnte Miteinbezug anderer Resultate, klinischer Gegebenheiten. So lässt sich Berechnung oder wenigstens Abschätzung einer postanalytischen Wahrscheinlichkeit erheben. Der gleiche Test wird eine sehr unterschiedliche Performance haben je nach dem gewählten Einsatzort, resp. dem gewählten Patientenkollektiv. So macht z. B. ein erstmaliger PSA-Test bei einem 70-jährigen Patienten wenig Sinn. Weiter spielen auch die Qualitäten des Tests eine entscheidende Rolle. Es kann davon ausgegangen werden, dass gerade in der Labormedizin weitere Outcome-Studien folgen werden. Der Wert dieser nimmt zu, da die Tests in der Regel nicht bis wenig invasiv, relativ günstig und rasch erhältlich sind.

Effects of Observer Variation on Performance in Probabilistic Diagnosis of Jaundice

1981 ◽  
Vol 20 (03) ◽  
pp. 163-168 ◽  
Author(s):  
G. Llndberg
Keyword(s):  
Physical Examination ◽  
System Performance ◽  
Transition Matrix ◽  
Bayes Theorem ◽  
Observer Variation ◽  
Kappa Statistics ◽  
Diagnostic Categories ◽  
Number Of Patients ◽  
Probability Transition Matrix ◽  
Probabilistic Diagnosis

A system for probabilistic diagnosis of jaundice has been used for studying the effects of taking into account the unreliability of diagnostic data caused by observer variation. Fourteen features from history and physical examination were studied. Bayes’ theorem was used for calculating the probabilities of a patient’s belonging to each of four diagnostic categories.The construction sample consisted of 61 patients. An equal number of patients were tested in the evaluation sample. Observer variation on the fourteen features had been assessed in two previous studies. The use of kappa-statistics for measuring observer variation allowed the construction of a probability transition matrix for each feature. Diagnostic probabilities could then be calculated with and without the inclusion of weights for observer variation. Tests of system performance revealed that discriminatory power remained unchanged. However, the predictions rendered by the variation-weighted system were diffident. It is concluded that taking observer variation into account may weaken the sharpness of probabilistic diagnosis but it may also help to explain the value of probabilistic diagnosis in future applications.

Bayesian Diagnostic Probabilities without Assuming Independence of Symptoms

1991 ◽  
Vol 30 (01) ◽  
pp. 15-22 ◽  
Author(s):  
A. Gammerman ◽  
A. R. Thatcher
Keyword(s):  
Abdominal Pain ◽  
Acute Abdominal Pain ◽  
Final Diagnosis ◽  
Bayes Theorem ◽  
Bayes Method ◽  
Hospital Records ◽  
Computer Diagnosis ◽  
Bayes Model ◽  
Chi Squared ◽  
Past Data

The paper describes an application of Bayes’ Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis, to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the “Simple Bayes” model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the “Simple Bayes” method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

scholarly journals Correction: Accuracy of the FMF Bayes theorem-based model for predicting preeclampsia at 11–13 weeks of gestation in a Japanese population

2021 ◽  
Author(s):  
Minako Goto ◽  
Keiko Koide ◽  
Mayumi Tokunaka ◽  
Hiroko Takita ◽  
Shoko Hamada ◽  
...  
Keyword(s):  
Japanese Population ◽  
Bayes Theorem

scholarly journals Bayes Theorem and Protopathic Bias: Methodological Concerns When Addressing the Impact of Fetal Heart Rate Patterns on the Cesarean Section Rate

2020 ◽  
Vol 10 (03) ◽  
pp. e342-e345
Author(s):  
Jacques Balayla ◽  
Ariane Lasry ◽  
Yaron Gil ◽  
Alexander Volodarsky-Perel
Keyword(s):  
Heart Rate ◽  
Caesarean Section ◽  
Fetal Heart Rate ◽  
Predictive Power ◽  
Fetal Heart ◽  
Caesarean Section Rate ◽  
Bayes Theorem ◽  
The Poor ◽  
Clinical Scenarios ◽  
The Impact

AbstractOver the last 30 years, the caesarean section rate has reached global epidemic proportions. This trend is driven by multiple factors, an important one of which is the use and inconsistent interpretation of the electronic fetal monitoring (EFM) system. Despite its introduction in the 1960s, the EFM has not definitively improved neonatal outcomes, yet it has since significantly contributed to a seven-fold increase in the caesarean section rate. As we attempt to reduce the caesarean rates in the developed world, we should consider focusing on areas that have garnered little attention in the literature, such as physician sensitization to the poor predictive power of the EFM and the research method biases that are involved in studying the abnormal heart rate patterns—umbilical cord pH relationship. Herein, we apply Bayes theorem to different clinical scenarios to illustrate the poor predictive power of the EFM, as well as shed light on the principle of protopathic bias, which affects the classification of research outcomes among studies addressing the effects of the EFM on caesarean rates. We propose and discuss potential solutions to the aforementioned considerations, which include the re-examination of guidelines with which we interpret fetal heart rate patterns and the development of noninvasive technologies that evaluate fetal pH in real time.

scholarly journals Formulating the history matching problem with consistent error statistics

2021 ◽  
Author(s):  
Geir Evensen
Keyword(s):  
Simulation Model ◽  
Measurement Errors ◽  
History Matching ◽  
Critical Role ◽  
Bayes Theorem ◽  
Conditional Probability Density ◽  
Model Parameters ◽  
Rate Data ◽  
Matching Problem ◽  
Reservoir Prediction

AbstractIt is common to formulate the history-matching problem using Bayes’ theorem. From Bayes’, the conditional probability density function (pdf) of the uncertain model parameters is proportional to the prior pdf of the model parameters, multiplied by the likelihood of the measurements. The static model parameters are random variables characterizing the reservoir model while the observations include, e.g., historical rates of oil, gas, and water produced from the wells. The reservoir prediction model is assumed perfect, and there are no errors besides those in the static parameters. However, this formulation is flawed. The historical rate data only approximately represent the real production of the reservoir and contain errors. History-matching methods usually take these errors into account in the conditioning but neglect them when forcing the simulation model by the observed rates during the historical integration. Thus, the model prediction depends on some of the same data used in the conditioning. The paper presents a formulation of Bayes’ theorem that considers the data dependency of the simulation model. In the new formulation, one must update both the poorly known model parameters and the rate-data errors. The result is an improved posterior ensemble of prediction models that better cover the observations with more substantial and realistic uncertainty. The implementation accounts correctly for correlated measurement errors and demonstrates the critical role of these correlations in reducing the update’s magnitude. The paper also shows the consistency of the subspace inversion scheme by Evensen (Ocean Dyn. 54, 539–560 2004) in the case with correlated measurement errors and demonstrates its accuracy when using a “larger” ensemble of perturbations to represent the measurement error covariance matrix.

Bayes' Theorem for predicting human carcinogenicity

Mutagenesis ◽  
1989 ◽  
Vol 4 (3) ◽  
pp. 242-243
Author(s):  
David P. Lovell
Keyword(s):  
Bayes Theorem

Comment on ‘A qualitative approach to Bayes’ theorem' by Medow and Lucey

2012 ◽  
Vol 18 (2) ◽  
pp. 80-80
Author(s):  
Aquiles Rodrigo Henríquez ◽  
Juan Moreira ◽  
Jef Van den Ende
Keyword(s):  
Bayes Theorem ◽  
Qualitative Approach

Simultaneous Adoption of Herbicide-Resistant and Conservation-Tillage Cotton Technologies

2006 ◽  
Vol 38 (3) ◽  
pp. 629-643 ◽  
Author(s):  
Roland K. Roberts ◽  
Burton C. English ◽  
Qi Gao ◽  
James A. Larson
Keyword(s):  
Logit Model ◽  
Conservation Tillage ◽  
Bayes Theorem ◽  
Cotton Production ◽  
Herbicide Resistant ◽  
Tillage Practices ◽  
Herbicide Use ◽  
Residual Herbicide ◽  
The Relationship ◽  
Simultaneous Adoption

If adoption of herbicide-resistant seed and adoption of conservation-tillage practices are determined simultaneously, adoption of herbicide-resistant seed could indirectly reduce soil erosion and adoption of conservation-tillage practices could indirectly reduce residual herbicide use and increase farm profits. Our objective was to evaluate the relationship between these two technologies for Tennessee cotton production. Evidence from Bayes' theorem and a two-equation logit model suggested a simultaneous relationship. Mean elasticities for acres in herbicide-resistant seed with respect to the probability of adopting conservation-tillage practices and acres in conservation-tillage practices with respect to the probability of adopting herbicide-resistant seed were 1.74 and 0.24, respectively.

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