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This study presents a novel algorithm for image classification based on a quasi-Bayesian approach and the extraction of probability density functions (PDFs). First, representative PDFs are extracted from each image using its features. Next, a measure is developed to evaluate the similarity between the extracted PDFs. Finally, an algorithm is established for determining prior probabilities using fuzzy clustering techniques. By combining these improvements, we develop a more efficient algorithm for classifying image data. An image is assigned to a specific group if it has the highest value of prior probability and a similar level to that group. We explain the proposed algorithm step-by-step with a numerical example and clearly demonstrate its convergence. When applied to multiple image datasets, the proposed algorithm has shown stability and efficiency, outperforming many other statistical and machine learning methods. Additionally, we have developed a Matlab procedure to apply the proposed algorithm to real image datasets. These applications demonstrate the potential of research in various fields related to the digital revolution and artificial intelligence. [ABSTRACT FROM AUTHOR]

The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study's aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method. [ABSTRACT FROM AUTHOR]

Diagnosing type 1 diabetes in adults is difficult since type 2 diabetes is the predominant diabetes type, particularly with an older age of onset (approximately >30 years). Misclassification of type 1 diabetes in adults is therefore common and will impact both individual patient management and the reported features of clinically classified cohorts. In this article, we discuss the challenges associated with correctly identifying adult-onset type 1 diabetes and the implications of these challenges for clinical practice and research. We discuss how many of the reported differences in the characteristics of autoimmune/type 1 diabetes with increasing age of diagnosis are likely explained by the inadvertent study of mixed populations with and without autoimmune aetiology diabetes. We show that when type 1 diabetes is defined by high-specificity methods, clinical presentation, islet-autoantibody positivity, genetic predisposition and progression of C-peptide loss remain broadly similar and severe at all ages and are unaffected by onset age within adults. Recent clinical guidance recommends routine islet-autoantibody testing when type 1 diabetes is clinically suspected or in the context of rapid progression to insulin therapy after a diagnosis of type 2 diabetes. In this moderate or high prior-probability setting, a positive islet-autoantibody test will usually confirm autoimmune aetiology (type 1 diabetes). We argue that islet-autoantibody testing of those with apparent type 2 diabetes should not be routinely undertaken as, in this low prior-prevalence setting, the positive predictive value of a single-positive islet antibody for autoimmune aetiology diabetes will be modest. When studying diabetes, extremely high-specificity approaches are needed to identify autoimmune diabetes in adults, with the optimal approach depending on the research question. We believe that until these recommendations are widely adopted by researchers, the true phenotype of late-onset type 1 diabetes will remain largely misunderstood. [ABSTRACT FROM AUTHOR]

Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate. [ABSTRACT FROM AUTHOR]

Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations between diagnostic tests and the varying prevalence of diseases. To explore this further, we have developed a freely available specialized computational tool that employs Bayesian inference to calculate the posterior probability of disease diagnosis. This novel software comprises of three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions effectively. The tool is equipped to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations. We demonstrate the utility of this software by analyzing fasting plasma glucose, and glycated hemoglobin A1c data from the National Health and Nutrition Examination Survey. Our results are validated using the oral glucose tolerance test as a reference standard, and we explore both parametric and nonparametric distribution models for the Bayesian diagnosis of diabetes mellitus. [ABSTRACT FROM AUTHOR]

The quality of the input data is amongst the decisive factors affecting the speed and effectiveness of recurrent neural network (RNN) learning. We present here a novel methodology to select optimal training data (those with the highest learning capacity) by approaching the problem from a decision making point of view. The key idea, which underpins the design of the mathematical structure that supports the selection, is to define first a binary relation that gives preference to inputs with higher estimator abilities. The Von Newman Morgenstern theorem (VNM), a cornerstone of decision theory, is then applied to determine the level of efficiency of the training dataset based on the probability of success derived from a purpose-designed framework based on Markov networks. To the best of the author's knowledge, this is the first time that this result has been applied to data selection tasks. Hence, it is shown that Markov Networks, mainly known as generative models, can successfully participate in discriminative tasks when used in conjunction with the VNM theorem. The simplicity of our design allows the selection to be carried out alongside the training. Hence, since learning progresses with only the optimal inputs, the data noise gradually disappears: the result is an improvement in the performance while minimising the likelihood of overfitting. [ABSTRACT FROM AUTHOR]

This paper examines a twofold tension in Gerhard Schurz's (2019) recent proposal to solve Hume's problem of induction. Schurz concedes to the skeptic that there is no non-circular epistemic justification of the reliability of induction, but then argues for the optimality of meta-induction so that if any prediction method is reliable, then meta-induction is. There is a tension in this proposal between meta-induction and our inductive practice: Are we supposed to abandon our inductive practice in favor of meta-induction? Schurz claims that given the actual way the world has been, the meta-inductive method supports the standard inductive method. There then arises a second tension: The challenges Schurz cites against the reliability of induction—such as the anti-inductive hypothesis and the state-uniform probability distribution—cast doubt on Schurz's claim that meta-induction supports induction. The reasoning in this paper moves in the opposite direction: There are ways of answering these challenges in defense of Schurz's claim that meta-induction supports induction, but these ways also point to ways of defending induction directly without the mediation of meta-induction. [ABSTRACT FROM AUTHOR]

Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate.

A probabilistic neural network has been implemented to predict the malignancy of breast cancer cells, based on a data set, the features of which are used for the formulation and training of a model for a binary classification problem. The focus is placed on considerations when building the model, in order to achieve not only accuracy but also a safe quantification of the expected uncertainty of the calculated network parameters and the medical prognosis. The source code is included to make the results reproducible, also in accordance with the latest trending in machine learning research, named Papers with Code. The various steps taken for the code development are introduced in detail but also the results are visually displayed and critically analyzed also in the sense of explainable artificial intelligence. In statistical-classification problems, the decision boundary is the region of the problem space in which the classification label of the classifier is ambiguous. Problem aspects and model parameters which influence the decision boundary are a special aspect of practical investigation considered in this work. Classification results issued by technically transparent machine learning software can inspire more confidence, as regards their trustworthiness which is very important, especially in the case of medical prognosis. Furthermore, transparency allows the user to adapt models and learning processes to the specific needs of a problem and has a boosting influence on the development of new methods in relevant machine learning fields (transfer learning).

The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study’s aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method.

This study proposes a new method to classify image data for two groups and effectively apply it in identifying people infected with COVID-19 based on their lung image. First, we extract each image into a two-dimensional interval based on the Gray Level Co-occurrence Matrix (GLCM). Next, find the prior probability based on the fuzzy relationship between the classified element and the established groups. Finally, combining the above improvements, we propose a new principle similar to the Bayesian method for classification. An image is classified into a group if it has the most significant value for prior probability and the smallest value for the overlap distance between the representative interval for the image and the groups. Applying the set of lung X-ray images to distinguish people infected with COVID-19, the proposed algorithm has given outstanding results compared with many well-known methods. The result also shows that this research can be applied in practice, and has the potential for different fields. [ABSTRACT FROM AUTHOR]

Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations between diagnostic tests and the varying prevalence of diseases. To explore this further, we have developed a freely available specialized computational tool that employs Bayesian inference to calculate the posterior probability of disease diagnosis. This novel software comprises of three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions effectively. The tool is equipped to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations. We demonstrate the utility of this software by analyzing fasting plasma glucose, and glycated hemoglobin A1c data from the National Health and Nutrition Examination Survey. Our results are validated using the oral glucose tolerance test as a reference standard, and we explore both parametric and nonparametric distribution models for the Bayesian diagnosis of diabetes mellitus.

In this paper we compare causal models with reason models in the construction of Bayesian networks for legal evidence. In causal models, arrows in the network are drawn from causes to effects. In a reason model, the arrows are instead drawn towards the evidence, from factum probandum to factum probans. We explore the differences between causal models and reason models and observe several distinct advantages with reason models. Reason models are better aligned with the philosophy of Bayesian inference, as they model reasons for up-dating beliefs. Reason models are better suited for measuring the combined support of the evidence, and a prior probability of guilt that reflects the number of possible perpetrators is accommodated more easily with reason models. [ABSTRACT FROM AUTHOR]

Abstract Objective The present simulation study aimed to assess positive predictive value (PPV) and negative predictive value (NPV) for our newly introduced Accounting for Expected Adjusted Effect test (AEAE test) and compare it to PPV and NPV for a traditional zero-order significance test. Results The AEAE test exhibited greater PPV compared to a traditional zero-order significance test, especially with a strong true adjusted effect, low prior probability, high degree of confounding, large sample size, high reliability in the measurement of predictor X and outcome Y, and low reliability in the measurement of confounder Z. The zero-order significance test, on the other hand, exhibited higher NPV, except for some combinations of high degree of confounding and large sample size, or low reliability in the measurement of Z and high reliability in the measurement of X/Y, in which case the zero-order significance test can be completely uninformative. Taken together, the findings demonstrate desirable statistical properties for the AEAE test compared to a traditional zero-order significance test.

• Knowledge mining on varying sets of compound hypotheses is inspected. • Bayesian learning is equipped with technique allowing to extend the set of hypotheses. • The technique copes with learning with less data than the amount of parameters. • The use of data forecasters as a knowledge source in parameter estimation is refined. • A rich set of interesting related research and application problems is presented. The paper proposes the way how to assign a proper prior probability to a new, generally compound, hypothesis. To this purpose, it uses the minimum relative-entropy principle and a forecaster-based knowledge transfer. Methodologically, it opens a way towards enriching the standard Bayesian framework by the possibility to extend the set of models during learning without the need to restart. The presented use scenarios concern: (a) creating new hypotheses, (b) learning problems with an insufficient amount of data, and (c) sequential Monte Carlo estimation. They indicate a strong application potential of the proposed technique. Related interesting open research problems are listed. [ABSTRACT FROM AUTHOR]

Background: Application of clinical study findings to surgical decision making requires accurate interpretation of the results, integration of the findings within the context of pre-existing knowledge and use of statistics to answer clinically relevant questions. Bayesian analyses are optimally suited for interpretation of study findings, supporting translation to the bedside. Discussion: Surgical decision making is a complex process that draws on an individual clinician's medical knowledge, experience, data, and the patient's unique characteristics and preferences. Subjective and objective knowledge may be merged to derive a probability of benefit or harm of a treatment under consideration. Bayesian reasoning complements the clinical decision-making process by incorporating known evidence and data from a new study to determine the probability of an outcome of interest. Bayesian analyses are statistically robust and intuitive when translating findings of a study into clinical care. In contrast, frequentist statistics are poorly suited to translate study findings to clinical application. This review aims to highlight the benefits of incorporating Bayesian analyses into clinical research. Conclusion: Bayesian analyses offer clinically relevant information including the probability of benefit or harm of a treatment under consideration while accounting for uncertainty. This information may be incorporated easily and accurately into surgical decision making. [ABSTRACT FROM AUTHOR]

This project is an illustration of two important concepts in Bayesian Analysis: 1) the prior probability or prior odds, and 2) the posterior probability or posterior odds. We demonstrate that for any probabilistic test result, the actual, or practical, posterior likelihood of deception or truth telling will vary in mathematically predictable ways in response to the prior probability (e.g., base-rate). Results are shown for a distribution of prior odds from 1 in 10 to 9 in 10 using Bayes Factor (i.e., the posterior odds under the equal prior) as a likelihood function to calculate the Bayesian posterior conditional odds of deception or truth-telling. Results show that for any polygraph test score the posterior odds of deception are higher than expected when the prior probability is high, and they are lower than expected when the prior probability is low. [ABSTRACT FROM AUTHOR]

Bloom occurrence probability prediction is a critical issue for freshwater resource management and protection. As the mechanism of algal blooms is not understood, the construction of prediction model mainly depends on statistical data. Therefore, knowledge on prior bloom occurrence derived from statistical data plays a significant role in establishing a prediction model. In this study, a naïve Bayesian model incorporating prior information was constructed to predict algal bloom occurrence probabilities 1–7 days in advance under different weather conditions in Dianchi Lake. The proposed model utilised the following data from the MODIS images, the floating algae index (FAI) for the previous 7 days and five meteorological variables (mean wind speed, air pressure, relative humidity on the prediction day, accumulated sunshine hours in the previous 3 days, and accumulated air temperature in the previous 7 days). The prior probabilities of each pixel were calculated on a monthly timescale to highlight the bloom’s temporal-spatial differences, and the 1–7 day posterior probabilities were calculated by combining the prior and conditional probabilities. The predictive effect was tested by the area under the receiver-operating characteristic curve (AUC), and the results showed the number of pixels with estimation predictions classified as ‘Good+’ and ‘Excellent’ were 91.3%, 91.4%, 82.7%, 83.3%, 89.0%, 86.6%, and 90.0% for the predictions on days 1–7, respectively. Additionally, the independent validation datasets showed that the percentage of correctly classified instances (CCI%) of approximately 90% of the pixels were greater than 50%. The proposed algal bloom prediction model based on the naïve Bayesian method has pixel-level prediction abilities, is applicable to other inland water systems, and can provide a reference for water environment risk forecasting and management.

Objective: The present simulation study aimed to assess positive predictive value (PPV) and negative predictive value (NPV) for our newly introduced Accounting for Expected Adjusted Effect test (AEAE test) and compare it to PPV and NPV for a traditional zero-order significance test. Results: The AEAE test exhibited greater PPV compared to a traditional zero-order significance test, especially with a strong true adjusted effect, low prior probability, high degree of confounding, large sample size, high reliability in the measurement of predictor X and outcome Y, and low reliability in the measurement of confounder Z. The zero-order significance test, on the other hand, exhibited higher NPV, except for some combinations of high degree of confounding and large sample size, or low reliability in the measurement of Z and high reliability in the measurement of X/Y, in which case the zero-order significance test can be completely uninformative. Taken together, the findings demonstrate desirable statistical properties for the AEAE test compared to a traditional zero-order significance test. [ABSTRACT FROM AUTHOR]

Investigation of past accidents has shown that LPG tank accidents cause significant damage to the industry due to the storage of large volumes of flammable materials in them. This study aimed for developing a predictive accident model for dynamic risk assessment of propane storage tanks of the refinery. Hazards and safety barriers were identified using MIMAH methodology. The basis of MIMAH methodology is Bow-tie method. To construct the Bow-tie diagram, first, accidents that occurred on LPG tanks were extracted from the databases of accidents and valid sources. The top events of the accidents were identified and analyzed by the fault tree. The Bow-tie diagrams were constructed and the barriers on the diagrams were identified and verified by refinery experts. According to the SHIPP model, safety barriers were categorized into seven main barriers. The failure rates of the fault tree basic events were extracted from reliable sources and the prior probability of barriers was calculated. Based on the failure or success of safety barriers, 6 levels of severity of consequences, safe, near miss, mishap, incident, and catastrophic accident were considered. Using the prior probability of failure of the barriers, the probability of occurrence of each level of severity of consequences was calculated with the event tree. In the next step, this paper employed LPG storage tanks past accidents to construct a likelihood function and update prior probability using the Bayesian equation. Finally, the posterior probability of occurrence of the consequences was calculated using the posterior probability of failure of the barriers. Because LPG accidents occur with low probability and high severity, predicting accidents dynamically helps people to always be prepared to prevent their occurrence. [ABSTRACT FROM AUTHOR]

Keywords: reproducibility; posterior probability; Bayes Factor Bound; positive predictive value; prior probability EN reproducibility posterior probability Bayes Factor Bound positive predictive value prior probability 49 54 6 02/24/21 20210101 NES 210101 1 Introduction Gibson ([7]) seeks to tackle a difficult and persistent problem in clinical research and indeed the scientific community as a whole - what is the strength of evidence for a scientific hypothesis based on observed data. Let I p i SB 0 sb be the prior probability that I H i SB 0 sb is false and let I p i = I p i -value from the test of I H i SB 0 sb from the current experiment. (Even those who say that a I p i -value should be reported as a continuous measure of evidence and not dichotomized must recall that the I p i -value is I not i a probability measure about I H i SB 0 sb .). [Extracted from the article]

Background: Low‐back pain (LBP) pathophysiological conditions include nociceptive back pain, somatic referred pain, radicular pain (RP), and radiculopathy. Differential diagnosis is challenging; guidance may come from patients' thorough clinical history and physical examination and, particularly for lumbar RP, from the evaluation of subjective responses of injured lumbar nerves to a strain applied at the buttock (buttock applied strain [BUAS] test). Methods: In a sample of 395 consecutive patients with LBP, sensitivity, specificity, and prior probability (positive predictive values [PPVs] and negative predictive values [NPVs]) of the BUAS test were evaluated against 2 reference tests: the straight leg raising test (SLRT) and the painDETECT (PD) questionnaire. Multinomial logistic regression (MLR) and χ2 analyses were used to evaluate the BUAS test outcomes' dependence upon independent variables (gender, age group, pain localization, SLRT outcomes, and PD outcomes). Cohen's kappa statistic was used to assess inter‐rater agreement. Results: Compared with the PD questionnaire, the BUAS test showed a sensitivity of 92%, specificity of 100%, PPV of 100%, and NPV of 82%; compared with the SLRT, the BUAS test showed a sensitivity of 82%, NPV of 82%, specificity of 40%, and PPV of 40%. Inter‐rater agreement of Cohen's kappa was 0.911. Significant associations were found between BUAS test outcomes and pain localization, SLRT outcomes, and PD outcomes, but not with the predictors gender or age group. MLR showed significant congruent relationships between BUAS test and PD outcomes. Conclusion: Among patients with LBP, the BUAS test showed satisfactory sensitivity, specificity, prior probability, and inter‐rater reliability; thus, it may be considered a useful adjunctive tool to diagnose RP in patients with LBP. For more generalized results, more research, in clinical settings other than pain clinics, is needed. [ABSTRACT FROM AUTHOR]

The integration of sensory with top–down cognitive signals for generating appropriate sensory–motor behaviors is an important issue in understanding the brain’s information processes. Recent studies have demonstrated that the interplay between sensory and high-level signals in oculomotor behavior could be explained by Bayesian inference. Specifically, prior knowledge for motion speed introduces a bias in the speed of smooth pursuit eye movements. The other important prediction of Bayesian inference is variability reduction by prior expectation; however, there is insufficient evidence in oculomotor behaviors to support this prediction. In the present study, we trained monkeys to switch the prior expectation about motion direction and independently controlled the strength of the motion stimulus. Under identical sensory stimulus conditions, we tested if prior knowledge about the motion direction reduced the variability of open-loop smooth pursuit eye movements. We observed a significant reduction when the prior expectation was strong; this was consistent with the prediction of Bayesian inference. Taking advantage of the open-loop smooth pursuit, we investigated the temporal dynamics of the effect of the prior to the pursuit direction bias and variability. This analysis demonstrated that the strength of the sensory evidence depended not only on the strength of the sensory stimulus but also on the time required for the pursuit system to form a neural sensory representation. Finally, we demonstrated that the variability and directional bias change by prior knowledge were quantitatively explained by the Bayesian observer model.

This study proposes some results in classifying by Bayesian method. There are upper and lower bounds of the Bayes error as well as its determination in case of one dimension and multi-dimensions. Based on the proposals for estimating of probability density functions, calculating the Bayes error and determining the prior probability, we establish an algorithm to evaluate ability of customers to pay debts at banks. This algorithm has been performed by the Matlab procedure that can be applied well with real data. The proposed algorithm is tested by the real application at a bank in Viet Nam that obtains the best results in comparing with the existing approaches.

Copyright of Transactions of Nanjing University of Aeronautics & Astronautics is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Presumptive drug testing is commonly used in both the clinical and forensic fields to allow rapid identification of the presence and/or usage of drugs. Because the tests generally have a high sensitivity and specificity (often >90%), then a positive test result may be taken to mean there is a high probability that a targeted drug is present. This assumption is, however, incorrect. This paper demonstrates how, in order to assess the positive predictive value (PPV) of a test, it is necessary to take into account, along with the sensitivity and specificity of the test, the prevalence of the drug in the population being investigated. We demonstrate how an alternative, Bayesian approach to assessing the posterior probability of a drug being present mimics the conventional calculation of PPVs but, because a Bayesian approach requires case‐specific prior probabilities, the posterior probabilities are more meaningful than PPV in any one specific case. The effectiveness of presumptive test results in cases such as drink‐spiking, drug‐driving, testing of drugs during seizures and the confirmation of initial presumptive test results is explored. In order to exploit the potential of presumptive drug testing, it is important that the prevalence of the targeted drugs in relevant populations is understood but, more importantly, it is important to consider using a Bayesian approach in order to tailor results to the specific individual or drug batch being tested. This article is categorized under:Toxicology > Analytical ToxicologyJurisprudence and Regulatory Oversight > Expert Evidence and Narrative [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This is part of a series of monographs on research design and analysis. The purpose of this article is to describe the purposes of and approach to conducting Bayesian decision making and analysis. Bayesian decision making involves basing decisions on the probability of a successful outcome, where this probability is informed by both prior information and new evidence the decision maker obtains. The statistical analysis that underlies the calculation of these probabilities is Bayesian analysis. In recent years, the Bayesian approach has been applied more commonly in both nutrition research and clinical decision making, and registered dietitian nutritionists would benefit from gaining a deeper understanding of this approach. This article provides a background of Bayesian decision making and analysis, and then presents applications of the approach in two different areas—medical diagnoses and nutrition policy research. It concludes with a description of how Bayesian decision making may be used in everyday life to allow each of us to appropriately weigh established beliefs and prior knowledge with new data and information in order to make well-informed and wise decisions. [ABSTRACT FROM AUTHOR]

One of the greatest challenges to the use of probabilistic reasoning in the assessment of criminal evidence is the 'problem of the prior', i.e. the difficulty in establishing an acceptable prior probability of guilt. Even strong supporters of a Bayesian approach have often preferred to ignore priors and focus on the likelihood ratio (LR) of the evidence. But to calculate if the probability of guilt, given the evidence reaches the probability required for conviction (the standard of proof), the LR has to be combined with a prior. In this article, we propose a solution to the 'problem of the prior': the defendant shall be treated as a member of the set of 'possible perpetrators' defined as the people who had the same or better opportunity as the defendant to commit the crime. For this purpose, we introduce the concept of an 'extended crime scene'. The number of people who had the same or better opportunity as the defendant is the number of people who were just as close or closer to the crime scene, in time and space. We demonstrate how the opportunity prior is incorporated into a generic Bayesian network model that allows us to integrate other evidence about the case. [ABSTRACT FROM AUTHOR]

The integration of sensory with top–down cognitive signals for generating appropriate sensory–motor behaviors is an important issue in understanding the brain's information processes. Recent studies have demonstrated that the interplay between sensory and high-level signals in oculomotor behavior could be explained by Bayesian inference. Specifically, prior knowledge for motion speed introduces a bias in the speed of smooth pursuit eye movements. The other important prediction of Bayesian inference is variability reduction by prior expectation; however, there is insufficient evidence in oculomotor behaviors to support this prediction. In the present study, we trained monkeys to switch the prior expectation about motion direction and independently controlled the strength of the motion stimulus. Under identical sensory stimulus conditions, we tested if prior knowledge about the motion direction reduced the variability of open-loop smooth pursuit eye movements. We observed a significant reduction when the prior expectation was strong; this was consistent with the prediction of Bayesian inference. Taking advantage of the open-loop smooth pursuit, we investigated the temporal dynamics of the effect of the prior to the pursuit direction bias and variability. This analysis demonstrated that the strength of the sensory evidence depended not only on the strength of the sensory stimulus but also on the time required for the pursuit system to form a neural sensory representation. Finally, we demonstrated that the variability and directional bias change by prior knowledge were quantitatively explained by the Bayesian observer model. [ABSTRACT FROM AUTHOR]