Use Of Probabilistic Reasoning Research Articles

Construct stochastic processes model to solve graphic and math problems

The use of probabilistic reasoning has been used to a variety of applications, such as image identification, computer diagnostics, stock price prediction, movie recommendation, and cyber intrusion detection. However, the range of probabilistic programming was limited until recently (partly due to the low computing capacity), and the bulk of inference techniques needed to be developed manually for each task. However, in 2015, using a 50-line probabilistic computer vision program, 3D representations of human faces were produced from 2D images of those faces.Use a computer to model various complex stochastic phenomena. All projects are programmed in java language. Find the actual number of 1s in a binary number. Formulate and analyze mathematical models to real life phenomena like Data Matrix and QR code. Those code mentioned above can provide a link to the real-life website or some certain message needed to be shown. using some random numbers to model uncertainty. To model some fractals. The first fractal maybe not self-avoiding. But the second one will never meet the same path for twice. To apply the concept stochastic processes. More java features and some random fractals and regression. Multiple factor analysis.

A Review of Information Fusion Methods for Gas Turbine Diagnostics

The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.

The opportunity prior: a proof-based prior for criminal cases

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.

On Cognitive Dynamic Systems: Cognitive Neuroscience and Engineering Learning From Each Other

Cognitive dynamic systems provide a broadly defined platform, whereby engineering learns from cognitive neuroscience, and by the same token, cognitive neuroscience learns from engineering. The first part of the paper is of a tutorial nature, addressing recent advances in cognitive perception and cognitive control, which are the dual of each other. The study of cognitive perception, viewed from the perspective of Bayesian inference, starts with sparse coding, well known in neuroscience. However, sparse coding could become ill-posed, particularly when the signal-to-noise ratio is low. In such situations, stability is a necessary requirement, which can only be satisfied if there is sufficient information in the observables. To satisfy this requirement, the sparse-coding algorithm is augmented by the addition of information filtering (i.e., a special case of Bayesian filtering). Accordingly, the performance of sparse coding is improved under the influence of perceptual attention. This improvement enhances the cognitive perceptor to separate relevant information from irrelevant information. Next, moving into cognitive control, viewed from the perspective of Bellman's dynamic programming, two ideas are exploited: entropic state of the perceptor, and the definition of reward as an invertible function of two entropic states, namely, the current state and its immediate past value. The net result of building on these two ideas is a modified form of Bellman's dynamic programming, and, therefore, a new reinforcement learning algorithm, which not only outperforms traditional reinforcement learning algorithms, but also offers some highly desirable properties. Among them is a linear law of computational complexity, which is the best that it could be. The second part of the paper addresses two challenging problems: first, how to mediate between cognitive control and cognitive perception and, second, how to formulate a procedure for risk control. The first problem is resolved by making use of probabilistic reasoning, a branch of probability theory, which leads into the formulation of a probabilistic reasoning machine. With this mediation in place, the conditions for overall system stability are derived, thereby confirming the probabilistic reasoning machine as the overall system stabilizer. The second challenge is risk control, which is by far the most challenging of them all: In the presence of an unexpected disturbance in the environment, risk is brought under control by mimicking the predict and preadapt function, which is considered to be the overarching function in the prefrontal cortex of the brain. To be specific, motor control is expanded by the inclusion of a new preadaptive control mechanism, which involves two different sets of actions: One set is made up of possible actions identified by the policy in the motor control. The other set involves a window of experiences (i.e., optimal actions) gained in the past. In a novel way, by exploiting these two sets, we end up with a preadaptive control mechanism in the form of a closed-loop feedback structure, which brings with it control (executive) attention.

Guidelines for a graph‐theoretic implementation of structural equation modeling

Structural equation modeling (SEM) is increasingly being chosen by researchers as a framework for gaining scientific insights from the quantitative analyses of data. New ideas and methods emerging from the study of causality, influences from the field of graphical modeling, and advances in statistics are expanding the rigor, capability, and even purpose of SEM. Guidelines for implementing the expanded capabilities of SEM are currently lacking. In this paper we describe new developments in SEM that we believe constitute a third‐generation of the methodology. Most characteristic of this new approach is the generalization of the structural equation model as a causal graph. In this generalization, analyses are based on graph theoretic principles rather than analyses of matrices. Also, new devices such as metamodels and causal diagrams, as well as an increased emphasis on queries and probabilistic reasoning, are now included. Estimation under a graph theory framework permits the use of Bayesian or likelihood methods. The guidelines presented start from a declaration of the goals of the analysis. We then discuss how theory frames the modeling process, requirements for causal interpretation, model specification choices, selection of estimation method, model evaluation options, and use of queries, both to summarize retrospective results and for prospective analyses.The illustrative example presented involves monitoring data from wetlands on Mount Desert Island, home of Acadia National Park. Our presentation walks through the decision process involved in developing and evaluating models, as well as drawing inferences from the resulting prediction equations. In addition to evaluating hypotheses about the connections between human activities and biotic responses, we illustrate how the structural equation (SE) model can be queried to understand how interventions might take advantage of an environmental threshold to limit Typha invasions.The guidelines presented provide for an updated definition of the SEM process that subsumes the historical matrix approach under a graph‐theory implementation. The implementation is also designed to permit complex specifications and to be compatible with various estimation methods. Finally, they are meant to foster the use of probabilistic reasoning in both retrospective and prospective considerations of the quantitative implications of the results.

The Status of Charity II: Charity, Probability, and Simplicity1

Treating the principle of charity as a non‐empirical, foundational principle leads to insoluble problems of justification. I suggest instead treating semantic properties realistically, and semantic terms as theoretical terms. This allows us to apply ordinary scientific reasoning in meta‐semantics. In particular, we can appeal to widespread verbal agreement as an empirical phenomenon, and we can make use of probabilistic reasoning as well as appeal to theoretical simplicity for reaching the conclusion that there is a high rate of agreement in belief between speakers who have a high rate of verbal agreement. Although this does not by itself imply that the beliefs agreed upon are generally true, it does imply that any single speaker who is party to such a verbal agreement is justified in taking the other speakers to have mostly true beliefs. She is so justified because of the fact that it is incoherent to take her own beliefs not to be mostly true. Indirectly, this justifies a version of the principle of charity as an empirically correct principle.

The Use of Probabilistic Reasoning to Improve Least Squares Based Gas Path Diagnostics

A method is proposed to support least square type of methods for deriving health parameters from a small number of independent gas path measurements. The method derives statistical information using sets of solutions derived from a number of data records, to produce sets of candidate solutions with a lesser number of parameters. These sets can then be processed to derive an accurate component fault diagnosis. It could thus be classified as a new type of "concentrator" approach, which is shown to be more effective than previously existing schemes. The method's effectiveness is demonstrated by application to a number of typical jet engine component faults.

False memories of the future: a critique of the applications of probabilistic reasoning to the study of cognitive processes.

The authors argue that the ways in which people-scientists and laymen-use probabilistic reasoning is predicated on a set of often questionable assumptions that are implicit and frequently go untested. They relate to the correspondence between the terms of a theory and the observations used to validate the theory and to the implicit understandings of intention and prior knowledge that arise between the conveyer and the receiver of information. The authors show several ways in which the use of probabilistic reasoning rests on a priori commitments to a partitioning of an outcome space and demonstrate that there are many more assumptions underlying the use of probabilistic reasoning than are usually acknowledged. They unfold these assumptions to show how several different interpretations of the same results in behavioral decision theory and cognitive psychology are equally well supported by "the facts." They then propose a more comprehensive approach to mapping cognitive processes than those currently used, one that is based on the analysis of all of the relevant alternative interpretations presented in the article.