Incorporation Of Prior Knowledge Research Articles

Optimal Reliability Demonstration Test Plans for $k$-out-of-$n$ Systems of Gamma Distributed Components

Constrained optimization problems are solved to determine the best reliability sampling plans for <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$k$</tex> </formula> -out-of- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$n$</tex> </formula> :F systems of gamma distributed components. As usual, the constraints are related to the quality levels required by the producer and the consumer, whereas the decision criterion is based on the uniformly most powerful reliability test. Excellent approximate solutions are derived in closed-forms assuming a frequentist perspective. This classical methodology is generalized to those situations in which the prior information on the fraction effective, <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r$</tex></formula> , is appreciable. Because prior knowledge on <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$r$</tex> </formula> often suggests a restricted parameter interval, a limited beta distribution is used to describe the probabilistic behavior of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$r$</tex></formula> . Optimal sample sizes and acceptance factors are obtained by solving mixed integer nonlinear programming problems. Lower and upper bounds on the smallest sample size, as well as a quite accurate approximation, are also provided. An efficient step-by-step procedure, which only needs a few iterations in most practical cases, is proposed for solving the optimization problem. The incorporation of prior knowledge into the reliability analysis provides better assessment of the true producer's and consumer's risks, and considerable savings in sample size and test duration. The optimal solutions are usually quite robust to small variations in the gamma shape parameter and the prior information. An example concerning a system composed of five water pumps for cooling a reactor is included for illustration. The proposed component test sampling plans can be recommended for use in practical applications, particularly when it is economically infeasible, or even impossible, to test a whole system.

Nonlinear dynamic systems modeling using Gaussian processes: Predicting ionospheric total electron content over South Africa

[1] Two different implementations of Gaussian process (GP) models are proposed to estimate the vertical total electron content (TEC) from dual frequency Global Positioning System (GPS) measurements. The model falseness of GP and neural network models are compared using daily GPS TEC data from Sutherland, South Africa, and it is shown that the proposed GP models exhibit superior model falseness. The GP approach has several advantages over previously developed neural network approaches, which include seamless incorporation of prior knowledge, a theoretically principled method for determining the much smaller number of free model parameters, the provision of estimates of the model uncertainty, and a more intuitive interpretability of the model.

Application of an efficient Bayesian discretization method to biomedical data

BackgroundSeveral data mining methods require data that are discrete, and other methods often perform better with discrete data. We introduce an efficient Bayesian discretization (EBD) method for optimal discretization of variables that runs efficiently on high-dimensional biomedical datasets. The EBD method consists of two components, namely, a Bayesian score to evaluate discretizations and a dynamic programming search procedure to efficiently search the space of possible discretizations. We compared the performance of EBD to Fayyad and Irani's (FI) discretization method, which is commonly used for discretization.ResultsOn 24 biomedical datasets obtained from high-throughput transcriptomic and proteomic studies, the classification performances of the C4.5 classifier and the naïve Bayes classifier were statistically significantly better when the predictor variables were discretized using EBD over FI. EBD was statistically significantly more stable to the variability of the datasets than FI. However, EBD was less robust, though not statistically significantly so, than FI and produced slightly more complex discretizations than FI.ConclusionsOn a range of biomedical datasets, a Bayesian discretization method (EBD) yielded better classification performance and stability but was less robust than the widely used FI discretization method. The EBD discretization method is easy to implement, permits the incorporation of prior knowledge and belief, and is sufficiently fast for application to high-dimensional data.

Utilization relevance vector machine for slope reliability analysis

This paper examines the potential of relevance vector machine (RVM) for slope reliability by using first order second moment method (FOSM). The FOSM demands the values and partial derivatives of the performance function with respect to the design random variables. Such calculations could be time-consuming or cumbersome when the performance functions are implicit. The analysis of slope by limit equilibrium method gives implicit performance functions. Here, RVM has been used to predict implicit performance functions. RVM rely on the Bayesian concept and utilize an inductive modelling procedure that allows incorporation of prior knowledge in the estimation process. In this paper, an example is given regarding how the proposed RVM-based FOSM analysis can be carried out. This study shows that the proposed RVM-based FOSM is viable alternative for slope reliability analysis.

The use of a Bayesian weighted least-squares approach to accelerate empirical engine model generation

An alternative to the commonly used design-of-experiments (DoE) technique based on the use of prior knowledge to speed up the model generation phase of the test programme is presented. Data are gathered in areas where the prior knowledge is least accurate and is blended into an overall empirical model using a Bayesian weighted least-squares (BWLS) approach. The work demonstrates the potential of the BWLS mechanism to reduce the test effort by the incorporation of prior knowledge from neighbouring operating points. Where the prior knowledge is a good representation of the current operating point the number of test points required to allow model convergence may be half that required by a DoE approach. Conversely, the Bayesian technique with inaccurate prior knowledge can still generate an accurate model at the expense of an increased number of test points over a standard experimental design, because the prior will incorrectly influence the model. The algorithm was tested with and without an outlier detection mechanism. This mechanism proved effective in detecting and rejecting simulated outliers and retesting the spurious data point.

From biological pathways to regulatory networks

This paper presents a general theoretical framework for generating Boolean networks whose state transitions realize a set of given biological pathways or minor variations thereof. This ill-posed inverse problem, which is of crucial importance across practically all areas of biology, is solved by using Karnaugh maps which are classical tools for digital system design. It is shown that the incorporation of prior knowledge, presented in the form of biological pathways, can bring about a dramatic reduction in the cardinality of the network search space. Constraining the connectivity of the network, the number and relative importance of the attractors, and concordance with observed time-course data are additional factors that can be used to further reduce the cardinality of the search space. The networks produced by the approaches developed here should facilitate the understanding of multivariate biological phenomena and the subsequent design of intervention approaches that are more likely to be successful in practice. As an example, the results of this paper are applied to the widely studied p53 pathway and it is shown that the resulting network exhibits dynamic behavior consistent with experimental observations from the published literature.

Actor-Critic Control with Reference Model Learning

AbstractWe propose a new actor-critic algorithm for reinforcement learning. The algorithm does not use an explicit actor, but learns a reference model which represents a desired behaviour, along which the process is to be controlled by using the inverse of a learned process model. The algorithm uses Local Linear Regression (LLR) to learn approximations of all the functions involved. The online learning of a process and reference model, in combination with LLR, provides an efficient policy update for faster learning. In addition, the algorithm facilitates the incorporation of prior knowledge. The novel method and a standard actor-critic algorithm are applied to the pendulum swingup problem, in which the novel method achieves faster learning than the standard algorithm.

Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge

Electronic auction markets are economic information systems that facilitate transactions between buyers and sellers. Whereas auction design has traditionally been an analytic process that relies on theory-driven assumptions such as bidders' rationality, bidders often exhibit unknown and variable behaviors. In this paper we present a data-driven adaptive auction mechanism that capitalizes on key properties of electronic auction markets, such as the large transaction volume, access to information, and the ability to dynamically alter the mechanism's design to acquire information about the benefits from different designs and adapt the auction mechanism online in response to actual bidders' behaviors. Our auction mechanism does not require an explicit representation of bidder behavior to infer about design profitability—a key limitation of prior approaches when they address complex auction settings. Our adaptive mechanism can also incorporate prior general knowledge of bidder behavior to enhance the search for effective designs. The data-driven adaptation and the capacity to use prior knowledge render our mechanisms particularly useful when there is uncertainty regarding bidders' behaviors or when bidders' behaviors change over time. Extensive empirical evaluations demonstrate that the adaptive mechanism outperforms any single fixed mechanism design under a variety of settings, including when bidders' strategies evolve in response to the seller's adaptation; our mechanism's performance is also more robust than that of alternatives when prior general information about bidders' behaviors differs from the encountered behaviors.

Bayesian mixture models for the incorporation of prior knowledge to inform genetic association studies

In the last decade, numerous genome-wide linkage and association studies of complex diseases have been completed. The critical question remains of how to best use this potentially valuable information to improve study design and statistical analysis in current and future genetic association studies. With genetic effect size for complex diseases being relatively small, the use of all available information is essential to untangle the genetic architecture of complex diseases. One promising approach to incorporating prior knowledge from linkage scans, or other information, is to up- or down-weight P-values resulting from genetic association study in either a frequentist or Bayesian manner. As an alternative to these methods, we propose a fully Bayesian mixture model to incorporate previous knowledge into on-going association analysis. In this approach, both the data and previous information collectively inform the association analysis, in contrast to modifying the association results (P-values) to conform to the prior knowledge. By using a Bayesian framework, one has flexibility in modeling, and is able to comprehensively assess the impact of model specification on posterior inferences. We illustrate the use of this method through a genome-wide linkage study of colorectal cancer, and a genome-wide association study of colorectal polyps.

Detection of Left Ventricular Motion Abnormality Via Information Measures and Bayesian Filtering

We present an original information theoretic measure of heart motion based on the Shannon's differential entropy (SDE), which allows heart wall motion abnormality detection. Based on functional images, which are subject to noise and segmentation inaccuracies, heart wall motion analysis is acknowledged as a difficult problem, and as such, incorporation of prior knowledge is crucial for improving accuracy. Given incomplete, noisy data and a dynamic model, the Kalman filter, a well-known recursive Bayesian filter, is devised in this study to the estimation of the left ventricular (LV) cavity points. However, due to similarity between the statistical information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem, which we investigate with a global measure based on the SDE. We further derive two other possible information theoretic abnormality detection criteria, one is based on Rényi entropy and the other on Fisher information. The proposed methods analyze wall motion quantitatively by constructing distributions of the normalized radial distance estimates of the LV cavity. Using 269 x 20 segmented LV cavities of short-axis MRI obtained from 30 subjects, the experimental analysis demonstrates that the proposed SDE criterion can lead to a significant improvement over other features that are prevalent in the literature related to the LV cavity, namely, mean radial displacement and mean radial velocity.

Computational intelligence in early diabetes diagnosis: a review.

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.

Simplified quantum process tomography

We propose and evaluate experimentally an approach to quantum process tomography that completely removes the scaling problem plaguing the standard approach. The key to this simplification is the incorporation of prior knowledge of the class of physical interactions involved in generating the dynamics, which reduces the problem to one of parameter estimation. This allows part of the problem to be tackled using efficient convex methods, which, when coupled with a constraint on some parameters, allows globally optimal estimates for the Krauss operators to be determined from experimental data. Parameterizing the maps provides further advantages: it allows the incorporation of mixed states of the environment as well as some initial correlation between the system and environment, both of which are common physical situations following excitation of the system away from thermal equilibrium. Although the approach is not universal, in cases where it is valid it returns a complete set of positive maps for the dynamical evolution of a quantum system at all times.

Constraint-based probabilistic learning of metabolic pathways from tomato volatiles

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-009-0166-2) contains supplementary material, which is available to authorized users.

Exploring the analysis of structured metabolomics data

In metabolomics research a large number of metabolites are measured that reflect the cellular state under the experimental conditions studied. In many occasions the experiments are performed according to an experimental design to make sure that sufficient variation is induced in the metabolite concentrations. However, as metabolomics is a holistic approach, also a large number of metabolites are measured in which no variation is induced by the experimental design. The presence of such non-induced metabolites hampers traditional data analysis methods as PCA to estimate the true model of the induced variation. The greediness of PCA leads to a clear overfit of the metabolomics data and can lead to a bad selection of important metabolites. In this paper we explore how, why and how severe PCA overfits data with an underlying experimental design. Recently new data analysis methods have been introduced that can use prior information of the system to reduce the overfit. We show that incorporation of prior knowledge of the system under investigation leads to a better estimation of the true underlying structure and to less overfit. The experimental design information together with ASCA is used to improve the analysis of metabolomics data. To show the improved model estimation property of ASCA a thorough simulation study is used and the results are extended to a microbial metabolomics batch fermentation study. The ASCA model is much less affected by the non-induced variation and measurement error than PCA, leading to a much better model of the induced variation.

Literature-based priors for gene regulatory networks

The use of prior knowledge to improve gene regulatory network modelling has often been proposed. In this article we present the first research on the massive incorporation of prior knowledge from literature for Bayesian network learning of gene networks. As the publication rate of scientific papers grows, updating online databases, which have been proposed as potential prior knowledge in past research, becomes increasingly challenging. The novelty of our approach lies in the use of gene-pair association scores that describe the overlap in the contexts in which the genes are mentioned, generated from a large database of scientific literature, harnessing the information contained in a huge number of documents into a simple, clear format. We present a method to transform such literature-based gene association scores to network prior probabilities, and apply it to learn gene sub-networks for yeast, Escherichia coli and Human organisms. We also investigate the effect of weighting the influence of the prior knowledge. Our findings show that literature-based priors can improve both the number of true regulatory interactions present in the network and the accuracy of expression value prediction on genes, in comparison to a network learnt solely from expression data. Networks learnt with priors also show an improved biological interpretation, with identified subnetworks that coincide with known biological pathways.

Prior knowledge processing for initial state of Kalman filter

AbstractThe paper deals with a specification of the prior distribution of the initial state for Kalman filter. The subjective prior knowledge, used in state estimation, can be highly uncertain. In practice, incorporation of prior knowledge contributes to a good start of the filter. The present paper proposes a methodology for selection of the initial state distribution, which enables eliciting of prior knowledge from the available expert information. The proposed methodology is based on the use of the conjugate prior distribution for models belonging to the exponential family. The normal state‐space model is used for demonstrating the methodology. The paper covers processing of the prior knowledge for state estimation, available in the form of simulated data. Practical experiments demonstrate the processing of prior knowledge from the urban traffic control area, which is the main application of the research. Copyright © 2009 John Wiley &amp; Sons, Ltd.

A Novel Method Incorporating Gene Ontology Information for Unsupervised Clustering and Feature Selection

BackgroundAmong the primary goals of microarray analysis is the identification of genes that could distinguish between different phenotypes (feature selection). Previous studies indicate that incorporating prior information of the genes' function could help identify physiologically relevant features. However, current methods that incorporate prior functional information do not provide a relative estimate of the effect of different genes on the biological processes of interest.ResultsHere, we present a method that integrates gene ontology (GO) information and expression data using Bayesian regression mixture models to perform unsupervised clustering of the samples and identify physiologically relevant discriminating features. As a model application, the method was applied to identify the genes that play a role in the cytotoxic responses of human hepatoblastoma cell line (HepG2) to saturated fatty acid (SFA) and tumor necrosis factor (TNF)-α, as compared to the non-toxic response to the unsaturated FFAs (UFA) and TNF-α. Incorporation of prior knowledge led to a better discrimination of the toxic phenotypes from the others. The model identified roles of lysosomal ATPases and adenylate cyclase (AC9) in the toxicity of palmitate. To validate the role of AC in palmitate-treated cells, we measured the intracellular levels of cyclic AMP (cAMP). The cAMP levels were found to be significantly reduced by palmitate treatment and not by the other FFAs, in accordance with the model selection of AC9.ConclusionsA framework is presented that incorporates prior ontology information, which helped to (a) perform unsupervised clustering of the phenotypes, and (b) identify the genes relevant to each cluster of phenotypes. We demonstrate the proposed framework by applying it to identify physiologically-relevant feature genes that conferred differential toxicity to saturated vs. unsaturated FFAs. The framework can be applied to other problems to efficiently integrate ontology information and expression data in order to identify feature genes.

Selecting explanatory variables with the modified version of the Bayesian information criterion

AbstractWe consider the situation in which a large database needs to be analyzed to identify a few important predictors of a given quantitative response variable. There is a lot of evidence that in this case classical model selection criteria, such as the Akaike information criterion or the Bayesian information criterion (BIC), have a strong tendency to overestimate the number of regressors. In our earlier papers, we developed the modified version of BIC (mBIC), which enables the incorporation of prior knowledge on a number of regressors and prevents overestimation. In this article, we review earlier results on mBIC and discuss the relationship of this criterion to the well‐known Bonferroni correction for multiple testing and the Bayes oracle, which minimizes the expected costs of inference. We use computer simulations and a real data analysis to illustrate the performance of the original mBIC and its rank version, which is designed to deal with data that contain some outlying observations. Copyright © 2008 John Wiley &amp; Sons, Ltd.

A Bayesian filtering approach for inclusion detection with ultrasound reflection tomography

Ultrasound is frequently used in nondestructive material testing due to its high sensitivity to inhomogeneities like cracks and gas inclusions. Tomographic approaches have the potential to attain higher resolution in terms of defect location and shape compared to conventional B-mode imaging. However, when commonplace reconstruction algorithms are used, the limited number of transducers available in realistic measurement setups leads to blurring and the presence of artifacts in the reconstructed image. This paper presents a novel reconstruction algorithm for ultrasound reflection tomography of binary material distributions. A key component is the Bayesian filtering approach based on particle filtering for the inverse problem solution. A parameterized model of material disturbances is employed. It allows the convenient incorporation of prior knowledge on the problem setup. Blurring and artifacts are inherently eliminated, allowing for more reliable and accurate reconstruction results.

A Bayesian Statistical Model for End Member Analysis of Sediment Geochemistry, Incorporating Spatial Dependences

Summary An important problem in the management of water supplies is identifying the sources of sediment. The paper develops a Bayesian approach, utilizing an end member model, to estimate the proportion of various sources of sediments in samples taken from a dam. This approach not only allows for the incorporation of prior knowledge about the geochemical compositions of the sources (or end members) but also allows for correlation between spatially contiguous samples and the prediction of the sediment's composition at unsampled locations. Sediments that were sampled from the North Pine Dam in south-east Queensland, Australia, are analysed to illustrate the approach.