Naive Bayesian classifiers for multinomial features: a theoretical analysis
CSIR Research Space (South Africa)
Van Dyk, E
2007-11-01
Full Text Available are individually binomial. Then, if we apply the naive Bayesian philosophy and define the likelihood function as the product of all binomial features, we get the likelihood function of class cr p(x¯|cr) = DY d=1 m! xd!(m− xd)!p xd dcrq m−xd dcr (1...) where x¯ is the input vector, xd is the frequency count for feature d, m is the number of Bernoulli trials done, pdcr is the proba- bility of feature d occurring in a Bernoulli trial for class cr and qdcr = 1− pdcr . The advantage of using eq. (1...
Binary naive Bayesian classifiers for correlated Gaussian features: a theoretical analysis
CSIR Research Space (South Africa)
Van Dyk, E
2008-11-01
Full Text Available error rate to that obtained when Monte-Carlo simulations are performed for a 2 and 12 dimensional binary classification problem. Finally, we illustrate the robust performances obtained with Naive Bayesian classifiers (as opposed to a maximum likelihood...
Directory of Open Access Journals (Sweden)
Ling Wang
Full Text Available BACKGROUND: Mammalian target of rapamycin (mTOR is a central controller of cell growth, proliferation, metabolism, and angiogenesis. Thus, there is a great deal of interest in developing clinical drugs based on mTOR. In this paper, in silico models based on multi-scaffolds were developed to predict mTOR inhibitors or non-inhibitors. METHODS: First 1,264 diverse compounds were collected and categorized as mTOR inhibitors and non-inhibitors. Two methods, recursive partitioning (RP and naïve Bayesian (NB, were used to build combinatorial classification models of mTOR inhibitors versus non-inhibitors using physicochemical descriptors, fingerprints, and atom center fragments (ACFs. RESULTS: A total of 253 models were constructed and the overall predictive accuracies of the best models were more than 90% for both the training set of 964 and the external test set of 300 diverse compounds. The scaffold hopping abilities of the best models were successfully evaluated through predicting 37 new recently published mTOR inhibitors. Compared with the best RP and Bayesian models, the classifier based on ACFs and Bayesian shows comparable or slightly better in performance and scaffold hopping abilities. A web server was developed based on the ACFs and Bayesian method (http://rcdd.sysu.edu.cn/mtor/. This web server can be used to predict whether a compound is an mTOR inhibitor or non-inhibitor online. CONCLUSION: In silico models were constructed to predict mTOR inhibitors using recursive partitioning and naïve Bayesian methods, and a web server (mTOR Predictor was also developed based on the best model results. Compound prediction or virtual screening can be carried out through our web server. Moreover, the favorable and unfavorable fragments for mTOR inhibitors obtained from Bayesian classifiers will be helpful for lead optimization or the design of new mTOR inhibitors.
Development of a prognostic naive bayesian classifier for successful treatment of nonunions.
Stojadinovic, Alexander; Kyle Potter, Benjamin; Eberhardt, John; Shawen, Scott B; Andersen, Romney C; Forsberg, Jonathan A; Shwery, Clay; Ester, Eric A; Schaden, Wolfgang
2011-01-19
predictive models permitting individualized prognostication for patients with fracture nonunion are lacking. The objective of this study was to train, test, and cross-validate a Bayesian classifier for predicting fracture-nonunion healing in a population treated with extracorporeal shock wave therapy. prospectively collected data from 349 patients with delayed fracture union or a nonunion were utilized to develop a naïve Bayesian belief network model to estimate site-specific fracture-nonunion healing in patients treated with extracorporeal shock wave therapy. Receiver operating characteristic curve analysis and tenfold cross-validation of the model were used to determine the clinical utility of the approach. predictors of fracture-healing at six months following shock wave treatment were the time between the fracture and the first shock wave treatment, the time between the fracture and the surgery, intramedullary stabilization, the number of bone-grafting procedures, the number of extracorporeal shock wave therapy treatments, work-related injury, and the bone involved (p < 0.05 for all comparisons). These variables were all included in the naïve Bayesian belief network model. a clinically relevant Bayesian classifier was developed to predict the outcome after extracorporeal shock wave therapy for fracture nonunions. The time to treatment and the anatomic site of the fracture nonunion significantly impacted healing outcomes. Although this study population was restricted to patients treated with shock wave therapy, Bayesian-derived predictive models may be developed for application to other fracture populations at risk for nonunion. prognostic Level II. See Instructions to Authors for a complete description of levels of evidence.
Fuzzy Naive Bayesian for constructing regulated network with weights.
Zhou, Xi Y; Tian, Xue W; Lim, Joon S
2015-01-01
In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted membership function (NEWFM) to extract regulated relations and weights. Then, we can use regulated relations and weights to construct a regulated network. Finally, we will classify the heart and Haberman datasets by the FNB network to compare with experiments of Naive Bayesian and TAN. The experiment results show that the FNB has a higher classification rate than Naive Bayesian and TAN.
Naive Bayesian for Email Filtering
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The paper presents a method of email filter based on Naive Bayesian theory that can effectively filter junk mail and illegal mail. Furthermore, the keys of implementation are discussed in detail. The filtering model is obtained from training set of email. The filtering can be done without the users specification of filtering rules.
Maximum margin Bayesian network classifiers.
Pernkopf, Franz; Wohlmayr, Michael; Tschiatschek, Sebastian
2012-03-01
We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.
A Bayesian classifier for symbol recognition
Barrat, Sabine; Tabbone, Salvatore; Nourrissier, Patrick
2007-01-01
URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...
Averaged Extended Tree Augmented Naive Classifier
Directory of Open Access Journals (Sweden)
Aaron Meehan
2015-07-01
Full Text Available This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN, which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN and Averaged One-Dependence Estimator (AODE classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required.
Institute of Scientific and Technical Information of China (English)
王双成; 高瑞; 杜瑞杰
2015-01-01
朴素贝叶斯分类器不能有效地利用属性之间的依赖信息,而目前所进行的依赖扩展更强调效率,使扩展后分类器的分类准确性还有待提高.针对以上问题,在使用具有平滑参数的高斯核函数估计属性密度的基础上,结合分类器的分类准确性标准和属性父结点的贪婪选择,进行朴素贝叶斯分类器的网络依赖扩展.使用UCI中的连续属性分类数据进行实验,结果显示网络依赖扩展后的分类器具有良好的分类准确性.%The naive Bayesian classifier can not effectively use the dependency information between attributes. At present, the efficiency of dependency extension is emphasized, which makes the classification accuracy of the extended classifier need to be improved. By using Gaussian kernel function with a smoothing parameter to estimate attribute density, the classification accuracy criterion and the greedy parent node selection of attributes are combined to extend the naive Bayesian classifier. An experiment is done by using data sets in UCI. The results show that extended classifiers have good classification accuracy.
3D Bayesian contextual classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
2000-01-01
We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....
ANALYSIS OF BAYESIAN CLASSIFIER ACCURACY
Directory of Open Access Journals (Sweden)
Felipe Schneider Costa
2013-01-01
Full Text Available The naÃ¯ve Bayes classifier is considered one of the most effective classification algorithms today, competing with more modern and sophisticated classifiers. Despite being based on unrealistic (naÃ¯ve assumption that all variables are independent, given the output class, the classifier provides proper results. However, depending on the scenario utilized (network structure, number of samples or training cases, number of variables, the network may not provide appropriate results. This study uses a process variable selection, using the chi-squared test to verify the existence of dependence between variables in the data model in order to identify the reasons which prevent a Bayesian network to provide good performance. A detailed analysis of the data is also proposed, unlike other existing work, as well as adjustments in case of limit values between two adjacent classes. Furthermore, variable weights are used in the calculation of a posteriori probabilities, calculated with mutual information function. Tests were applied in both a naÃ¯ve Bayesian network and a hierarchical Bayesian network. After testing, a significant reduction in error rate has been observed. The naÃ¯ve Bayesian network presented a drop in error rates from twenty five percent to five percent, considering the initial results of the classification process. In the hierarchical network, there was not only a drop in fifteen percent error rate, but also the final result came to zero.
Adaptive Naive Bayesian Anti-Spam Engine
Gajewski, W P
2006-01-01
The problem of spam has been seriously troubling the Internet community during the last few years and currently reached an alarming scale. Observations made at CERN (European Organization for Nuclear Research located in Geneva, Switzerland) show that spam mails can constitute up to 75% of daily SMTP traffic. A naïve Bayesian classifier based on a Bag of Words representation of an email is widely used to stop this unwanted flood as it combines good performance with simplicity of the training and classification processes. However, facing the constantly changing patterns of spam, it is necessary to assure online adaptability of the classifier. This work proposes combining such a classifier with another NBC (naïve Bayesian classifier) based on pairs of adjacent words. Only the latter will be retrained with examples of spam reported by users. Tests are performed on considerable sets of mails both from public spam archives and CERN mailboxes. They suggest that this architecture can increase spam recall without af...
Bayesian classifiers applied to the Tennessee Eastman process.
Dos Santos, Edimilson Batista; Ebecken, Nelson F F; Hruschka, Estevam R; Elkamel, Ali; Madhuranthakam, Chandra M R
2014-03-01
Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.
Learning Continuous Time Bayesian Network Classifiers Using MapReduce
Directory of Open Access Journals (Sweden)
Simone Villa
2014-12-01
Full Text Available Parameter and structural learning on continuous time Bayesian network classifiers are challenging tasks when you are dealing with big data. This paper describes an efficient scalable parallel algorithm for parameter and structural learning in the case of complete data using the MapReduce framework. Two popular instances of classifiers are analyzed, namely the continuous time naive Bayes and the continuous time tree augmented naive Bayes. Details of the proposed algorithm are presented using Hadoop, an open-source implementation of a distributed file system and the MapReduce framework for distributed data processing. Performance evaluation of the designed algorithm shows a robust parallel scaling.
General and Local: Averaged k-Dependence Bayesian Classifiers
Directory of Open Access Journals (Sweden)
Limin Wang
2015-06-01
Full Text Available The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB classifier can construct at arbitrary points (values of k along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB, tree augmented naive Bayes (TAN, Averaged one-dependence estimators (AODE, and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance.
Fault Diagnosis for Fuel Cell Based on Naive Bayesian Classification
Directory of Open Access Journals (Sweden)
Liping Fan
2013-07-01
Full Text Available Many kinds of uncertain factors may exist in the process of fault diagnosis and affect diagnostic results. Bayesian network is one of the most effective theoretical models for uncertain knowledge expression and reasoning. The method of naive Bayesian classification is used in this paper in fault diagnosis of a proton exchange membrane fuel cell (PEMFC system. Based on the model of PEMFC, fault data are obtained through simulation experiment, learning and training of the naive Bayesian classification are finished, and some testing samples are selected to validate this method. Simulation results demonstrate that the method is feasible.
WORD SENSE DISAMBIGUATION BASED ON IMPROVED BAYESIAN CLASSIFIERS
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar(DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%.
A native Bayesian classifier based routing protocol for VANETS
Bao, Zhenshan; Zhou, Keqin; Zhang, Wenbo; Gong, Xiaolei
2016-12-01
Geographic routing protocols are one of the most hot research areas in VANET (Vehicular Ad-hoc Network). However, there are few routing protocols can take both the transmission efficient and the usage of ratio into account. As we have noticed, different messages in VANET may ask different quality of service. So we raised a Native Bayesian Classifier based routing protocol (Naive Bayesian Classifier-Greedy, NBC-Greedy), which can classify and transmit different messages by its emergency degree. As a result, we can balance the transmission efficient and the usage of ratio with this protocol. Based on Matlab simulation, we can draw a conclusion that NBC-Greedy is more efficient and stable than LR-Greedy and GPSR.
3-D contextual Bayesian classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
In this paper we will consider extensions of a series of Bayesian 2-D contextual classification pocedures proposed by Owen (1984) Hjort & Mohn (1984) and Welch & Salter (1971) and Haslett (1985) to 3 spatial dimensions. It is evident that compared to classical pixelwise classification further...
Diagnosis of combined faults in Rotary Machinery by Non-Naive Bayesian approach
Asr, Mahsa Yazdanian; Ettefagh, Mir Mohammad; Hassannejad, Reza; Razavi, Seyed Naser
2017-02-01
When combined faults happen in different parts of the rotating machines, their features are profoundly dependent. Experts are completely familiar with individuals faults characteristics and enough data are available from single faults but the problem arises, when the faults combined and the separation of characteristics becomes complex. Therefore, the experts cannot declare exact information about the symptoms of combined fault and its quality. In this paper to overcome this drawback, a novel method is proposed. The core idea of the method is about declaring combined fault without using combined fault features as training data set and just individual fault features are applied in training step. For this purpose, after data acquisition and resampling the obtained vibration signals, Empirical Mode Decomposition (EMD) is utilized to decompose multi component signals to Intrinsic Mode Functions (IMFs). With the use of correlation coefficient, proper IMFs for feature extraction are selected. In feature extraction step, Shannon energy entropy of IMFs was extracted as well as statistical features. It is obvious that most of extracted features are strongly dependent. To consider this matter, Non-Naive Bayesian Classifier (NNBC) is appointed, which release the fundamental assumption of Naive Bayesian, i.e., the independence among features. To demonstrate the superiority of NNBC, other counterpart methods, include Normal Naive Bayesian classifier, Kernel Naive Bayesian classifier and Back Propagation Neural Networks were applied and the classification results are compared. An experimental vibration signals, collected from automobile gearbox, were used to verify the effectiveness of the proposed method. During the classification process, only the features, related individually to healthy state, bearing failure and gear failures, were assigned for training the classifier. But, combined fault features (combined gear and bearing failures) were examined as test data. The achieved
Dynamic Bayesian Combination of Multiple Imperfect Classifiers
Simpson, Edwin; Psorakis, Ioannis; Smith, Arfon
2012-01-01
Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications. In many situations, such as when human decisions need to be combined, the base decisions can vary enormously in reliability. A Bayesian approach to such uncertain combination allows us to infer the differences in performance between individuals and to incorporate any available prior knowledge about their abilities when training data is sparse. In this paper we explore Bayesian classifier combination, using the computationally efficient framework of variational Bayesian inference. We apply the approach to real data from a large citizen science project, Galaxy Zoo Supernovae, and show that our method far outperforms other established approaches to imperfect decision combination. We go on to analyse the putative community structure of the decision makers, based on their inferred decision making strategies, and show that natural groupings are formed. Finally we present ...
Face detection by aggregated Bayesian network classifiers
Pham, T.V.; Worring, M.; Smeulders, A.W.M.
2002-01-01
A face detection system is presented. A new classification method using forest-structured Bayesian networks is used. The method is used in an aggregated classifier to discriminate face from non-face patterns. The process of generating non-face patterns is integrated with the construction of the aggr
Executed Movement Using EEG Signals through a Naive Bayes Classifier
Directory of Open Access Journals (Sweden)
Juliano Machado
2014-11-01
Full Text Available Recent years have witnessed a rapid development of brain-computer interface (BCI technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brain’s normal output pathways of peripheral nerves and muscles, but on detectable signals that represent responsive or intentional brain activities. This paper presents a comparative study of the usage of the linear discriminant analysis (LDA and the naive Bayes (NB classifiers on describing both right- and left-hand movement through electroencephalographic signal (EEG acquisition. For the analysis, we considered the following input features: the energy of the segments of a band pass-filtered signal with the frequency band in sensorimotor rhythms and the components of the spectral energy obtained through the Welch method. We also used the common spatial pattern (CSP filter, so as to increase the discriminatory activity among movement classes. By using the database generated by this experiment, we obtained hit rates up to 70%. The results are compatible with previous studies.
Fcoused crawler bused on Bayesian classifier
Directory of Open Access Journals (Sweden)
JIA Haijun
2013-12-01
Full Text Available With the rapid development of the network,its information resources are increasingly large and faced a huge amount of information database,search engine plays an important role.Focused crawling technique,as the main core portion of search engine,is used to calculate the relationship between search results and search topics,which is called correlation.Normally,focused crawling method is used only to calculate the correlation between web content and search related topics.In this paper,focused crawling method is used to compute the importance of links through link content and anchor text,then Bayesian classifier is used to classify the links,and finally cosine similarity function is used to calculate the relevance of web pages.If the correlation value is greater than the threshold the page is considered to be associated with the predetermined topics,otherwise not relevant.Experimental results show that a high accuracy can be obtained by using the proposed crawling approach.
Stochastic margin-based structure learning of Bayesian network classifiers.
Pernkopf, Franz; Wohlmayr, Michael
2013-02-01
The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers. Furthermore, greedy hill-climbing and simulated annealing search heuristics are applied to determine the classifier structures. In the experiments, we demonstrate the advantages of maximum margin optimized Bayesian network structures in terms of classification performance compared to traditionally used discriminative structure learning methods. Stochastic simulated annealing requires less score evaluations than greedy heuristics. Additionally, we compare generative and discriminative parameter learning on both generatively and discriminatively structured Bayesian network classifiers. Margin-optimized Bayesian network classifiers achieve similar classification performance as support vector machines. Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.
Predicting the Severity of Breast Masses with Ensemble of Bayesian Classifiers
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Alaa M. Elsayad
2010-01-01
Full Text Available Problem statement: This study evaluated two different Bayesian classifiers; tree augmented Naive Bayes and Markov blanket estimation networks in order to build an ensemble model for prediction the severity of breast masses. The objective of the proposed algorithm was to help physicians in their decisions to perform a breast biopsy on a suspicious lesion seen in a mammogram image or to perform a short term follow-up examination instead. While, mammography is the most effective and available tool for breast cancer screening, mammograms do not detect all breast cancers. Also, a small portion of mammograms show that a cancer could probably be present when it is not (called a false-positive result. Approach: Apply ensemble of Bayesian classifiers to predict the severity of breast masses. Bayesian classifiers had been selected as they were able to produce probability estimates rather than predictions. These estimated allow predictions to be ranked and their expected costs to be minimized. The proposed ensemble used the confidence scores where the highest confidence wins to combine the predictions of individual classifiers. Results: The prediction accuracies of Bayesian ensemble was benchmarked against the well-known multilayer perceptron neural network and the ensemble had achieved a remarkable performance with 91.83% accuracy on training subset and 90.63% of test one and outperformed the neural network model. Conclusion: Experimental results showed that the Bayesian classifiers are competitive techniques in the problem of prediction the severity of breast masses.
Bayesian technique for image classifying registration.
Hachama, Mohamed; Desolneux, Agnès; Richard, Frédéric J P
2012-09-01
In this paper, we address a complex image registration issue arising while the dependencies between intensities of images to be registered are not spatially homogeneous. Such a situation is frequently encountered in medical imaging when a pathology present in one of the images modifies locally intensity dependencies observed on normal tissues. Usual image registration models, which are based on a single global intensity similarity criterion, fail to register such images, as they are blind to local deviations of intensity dependencies. Such a limitation is also encountered in contrast-enhanced images where there exist multiple pixel classes having different properties of contrast agent absorption. In this paper, we propose a new model in which the similarity criterion is adapted locally to images by classification of image intensity dependencies. Defined in a Bayesian framework, the similarity criterion is a mixture of probability distributions describing dependencies on two classes. The model also includes a class map which locates pixels of the two classes and weighs the two mixture components. The registration problem is formulated both as an energy minimization problem and as a maximum a posteriori estimation problem. It is solved using a gradient descent algorithm. In the problem formulation and resolution, the image deformation and the class map are estimated simultaneously, leading to an original combination of registration and classification that we call image classifying registration. Whenever sufficient information about class location is available in applications, the registration can also be performed on its own by fixing a given class map. Finally, we illustrate the interest of our model on two real applications from medical imaging: template-based segmentation of contrast-enhanced images and lesion detection in mammograms. We also conduct an evaluation of our model on simulated medical data and show its ability to take into account spatial variations
What are the Differences between Bayesian Classifiers and Mutual-Information Classifiers?
Hu, Bao-Gang
2011-01-01
In this study, both Bayesian classifiers and mutual information classifiers are examined for binary classifications with or without a reject option. The general decision rules in terms of distinctions on error types and reject types are derived for Bayesian classifiers. A formal analysis is conducted to reveal the parameter redundancy of cost terms when abstaining classifications are enforced. The redundancy implies an intrinsic problem of "non-consistency" for interpreting cost terms. If no data is given to the cost terms, we demonstrate the weakness of Bayesian classifiers in class-imbalanced classifications. On the contrary, mutual-information classifiers are able to provide an objective solution from the given data, which shows a reasonable balance among error types and reject types. Numerical examples of using two types of classifiers are given for confirming the theoretical differences, including the extremely-class-imbalanced cases. Finally, we briefly summarize the Bayesian classifiers and mutual-info...
Tian, Xue W; Lim, Joon S
2015-01-01
Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.
Steeneveld, W.; Gaag, van der L.C.; Barkema, H.W.; Hogeveen, H.
2009-01-01
Clinical mastitis (CM) can be caused by a wide variety of pathogens and farmers must start treatment before the actual causal pathogen is known. By providing a probability distribution for the causal pathogen, naive Bayesian networks (NBN) can serve as a management tool for farmers to decide which t
Max-margin based Bayesian classifier
Institute of Scientific and Technical Information of China (English)
Tao-cheng HU‡; Jin-hui YU
2016-01-01
There is a tradeoff between generalization capability and computational overhead in multi-class learning. We propose a generative probabilistic multi-class classifi er, considering both the generalization capability and the learning/prediction rate. We show that the classifi er has a max-margin property. Thus, prediction on future unseen data can nearly achieve the same performance as in the training stage. In addition, local variables are eliminated, which greatly simplifi es the optimization problem. By convex and probabilistic analysis, an eﬃcient online learning algorithm is developed. The algorithm aggregates rather than averages dualities, which is different from the classical situations. Empirical results indicate that our method has a good generalization capability and coverage rate.
Prediction of Protein-Protein Interaction Sites Based on Naive Bayes Classifier
Directory of Open Access Journals (Sweden)
Haijiang Geng
2015-01-01
Full Text Available Protein functions through interactions with other proteins and biomolecules and these interactions occur on the so-called interface residues of the protein sequences. Identifying interface residues makes us better understand the biological mechanism of protein interaction. Meanwhile, information about the interface residues contributes to the understanding of metabolic, signal transduction networks and indicates directions in drug designing. In recent years, researchers have focused on developing new computational methods for predicting protein interface residues. Here we creatively used a 181-dimension protein sequence feature vector as input to the Naive Bayes Classifier- (NBC- based method to predict interaction sites in protein-protein complexes interaction. The prediction of interaction sites in protein interactions is regarded as an amino acid residue binary classification problem by applying NBC with protein sequence features. Independent test results suggested that Naive Bayes Classifier-based method with the protein sequence features as input vectors performed well.
Klasifikasi Teks Bahasa Bali dengan Metode Information Gain dan Naive Bayes Classifier
Directory of Open Access Journals (Sweden)
Ida Bagus Gede Widnyana Putra
2016-11-01
Full Text Available Ketersediaan dokumen teks bahasa Bali yang meningkat jumlahnya membuat proses pencarian informasi pada dokumen teks berbahasa Bali menjadi semakin sulit. Mengklasifikasikanya secara manual menjadi tidak efisien mengingat peningkatan jumlah dokumen yang semakin banyak. Pada penelitian ini dikembangkan sebuah aplikasi yang dapat mengklasifikasikan teks bahasa Bali ke dalam kategori yang ditentukan. Aplikasi ini menggunakan metode klasifikasi Naive Bayes Classifier (NBC dan metode Information Gain(IG untuk seleksi fitur. Aplikasi ini diuji dengan teknik cross validation. Hasilnya adalah nilai rata-rata akurasi dari 10 fold cross validation sebesar 95,22%.
Using Bayesian neural networks to classify forest scenes
Vehtari, Aki; Heikkonen, Jukka; Lampinen, Jouko; Juujarvi, Jouni
1998-10-01
We present results that compare the performance of Bayesian learning methods for neural networks on the task of classifying forest scenes into trees and background. Classification task is demanding due to the texture richness of the trees, occlusions of the forest scene objects and diverse lighting conditions under operation. This makes it difficult to determine which are optimal image features for the classification. A natural way to proceed is to extract many different types of potentially suitable features, and to evaluate their usefulness in later processing stages. One approach to cope with large number of features is to use Bayesian methods to control the model complexity. Bayesian learning uses a prior on model parameters, combines this with evidence from a training data, and the integrates over the resulting posterior to make predictions. With this method, we can use large networks and many features without fear of overfitting. For this classification task we compare two Bayesian learning methods for multi-layer perceptron (MLP) neural networks: (1) The evidence framework of MacKay uses a Gaussian approximation to the posterior weight distribution and maximizes with respect to hyperparameters. (2) In a Markov Chain Monte Carlo (MCMC) method due to Neal, the posterior distribution of the network parameters is numerically integrated using the MCMC method. As baseline classifiers for comparison we use (3) MLP early stop committee, (4) K-nearest-neighbor and (5) Classification And Regression Tree.
A NON-PARAMETER BAYESIAN CLASSIFIER FOR FACE RECOGNITION
Institute of Scientific and Technical Information of China (English)
Liu Qingshan; Lu Hanqing; Ma Songde
2003-01-01
A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.
Kamruzzaman, S M; Hasan, Ahmed Ryadh
2010-01-01
Text classification is the automated assignment of natural language texts to predefined categories based on their content. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Now a day the demand of text classification is increasing tremendously. Keeping this demand into consideration, new and updated techniques are being developed for the purpose of automated text classification. This paper presents a new algorithm for text classification. Instead of using words, word relation i.e. association rules is used to derive feature set from pre-classified text documents. The concept of Naive Bayes Classifier is then used on derived features and finally a concept of Genetic Algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental ...
Learning Bayesian network classifiers for credit scoring using Markov Chain Monte Carlo search
Baesens, B.; Egmont-Petersen, M.; Castelo, R.; Vanthienen, J.
2002-01-01
In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using Markov Chain Monte Carlo (MCMC) search. The exp
Image Classifying Registration for Gaussian & Bayesian Techniques: A Review
Directory of Open Access Journals (Sweden)
Rahul Godghate,
2014-04-01
Full Text Available A Bayesian Technique for Image Classifying Registration to perform simultaneously image registration and pixel classification. Medical image registration is critical for the fusion of complementary information about patient anatomy and physiology, for the longitudinal study of a human organ over time and the monitoring of disease development or treatment effect, for the statistical analysis of a population variation in comparison to a so-called digital atlas, for image-guided therapy, etc. A Bayesian Technique for Image Classifying Registration is well-suited to deal with image pairs that contain two classes of pixels with different inter-image intensity relationships. We will show through different experiments that the model can be applied in many different ways. For instance if the class map is known, then it can be used for template-based segmentation. If the full model is used, then it can be applied to lesion detection by image comparison. Experiments have been conducted on both real and simulated data. It show that in the presence of an extra-class, the classifying registration improves both the registration and the detection, especially when the deformations are small. The proposed model is defined using only two classes but it is straightforward to extend it to an arbitrary number of classes.
Directory of Open Access Journals (Sweden)
Mariam Marlina
2017-05-01
Full Text Available AbstrakISPA (Infeksi Saluran Pernafasan Akut adalah suatu penyakit gangguan saluran pernapasan yang dapat menimbulkan berbagai spektrum penyakit mulai dari penyakit tanpa gejala, infeksi ringan sampai penyakit yang parah dan mematikan akibat faktor lingkungan. Kurangnya pengetahuan masyarakat mengenai gejala dan cara penanganan penyakit ISPA merupakan salah satu faktor penyebab tingginya angka kematian akibat ISPA. Peran sistem pakar yang disediakan dalam bentuk aplikasi sangat diperlukan untuk membantu seseorang dalam melakukan diagnosa penyakit ISPA secara mudah dan cepat. Dengan berusaha mengadopsi pengetahuan manusia ke komputer, sistem pakar mampu menyelesaikan permasalahan seperti yang dilakukan oleh seorang pakar. Oleh Karena itu, Aplikasi Sistem Pakar Diagnosis Penyakit ISPA Berbasis Speech Recognition Menggunakan Metode Naive Bayes Classifier dapat digunakan untuk mendiagnosis penyakit ISPA terhadap seseorang berdasarkan konversi hasil deteksi suara pengguna. Dengan aplikasi ini pengguna seakan berkonsultasi kepada seorang dokter/pakar yang menangani penyakit ISPA. Aplikasi dibangun berbasis android dengan menggunakan bahasa pemrograman Java dan database MySQL. Kata kunci : Sistem pakar, speech recognition, ISPA, metode naïve bayes classifier, Android. AbstractISPA (Acute Respiratory Tract Infection is a respiratory disorder disease that can lead to a wide spectrum of diseases ranging from asymptomatic disease, mild infection to severe and deadly disease due to environmental factors. So if someone complains of respiratory disorders not necessarily just have regular respiratory problems because it could be the person has ARI disease. The role of expert systems provided in the form of an application is needed to help a person in the diagnosis of ARI disease easily and quickly. By trying to adopt human knowledge into a computer, an expert system is capable of solving problems like that of an expert. Therefore, the Application of Expert
Akhtar, Naveed; Mian, Ajmal
2017-10-03
We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.
Schneider, Claudio Albert
This research is aimed at the solution of two common but still largely unsolved problems in the classification of remotely sensed data: (1) Classification accuracy of remotely sensed data decreases significantly in mountainous terrain, where topography strongly influences the spectral response of the features on the ground; and (2) when attempting to obtain more detailed classifications, e.g. forest cover types or species, rather than just broad categories of forest such as coniferous or deciduous, the accuracy of the classification generally decreases significantly. The main objective of the study was to develop a widely applicable and efficient classification procedure for mapping forest and other cover types in mountainous terrain, using an integrated GIS/neural network/Bayesian classification approach. The performance of this new technique was compared to a standard supervised Maximum Likelihood classification technique, a "conventional" Bayesian/Maximum Likelihood classification, and to a "conventional" neural network classifier. Results indicate a considerable improvement of the new technique over the standard Maximum Likelihood classification technique, as well as a better accuracy than the "conventional" Bayesian/Maximum Likelihood classifier (13.08 percent improvement in overall accuracy), but the "conventional" neural network classifiers outperformed all the techniques compared in this study, with an overall accuracy improvement of 15.94 percent as compared to the standard Maximum Likelihood classifier (from 46.77 percent to 62.71 percent). However, the overall accuracies of all the classification techniques compared in this study were relative low. It is believed that this was caused by problems related to the inadequacy of the reference data. On the other hand, the results also indicate the need to develop a different sampling design to more effectively cover the variability across all the parameters needed by the neural network classification technique
Bayesian network classifiers for categorizing cortical GABAergic interneurons.
Mihaljević, Bojan; Benavides-Piccione, Ruth; Bielza, Concha; DeFelipe, Javier; Larrañaga, Pedro
2015-04-01
An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1-F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts' assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell's label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1-F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52% accuracy, and single out the number of branches at 180 μm from the soma, the convex hull 2D area, and the axonal features F1-F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons.
Statistical Mechanical Development of a Sparse Bayesian Classifier
Uda, Shinsuke; Kabashima, Yoshiyuki
2005-08-01
The demand for extracting rules from high dimensional real world data is increasing in various fields. However, the possible redundancy of such data sometimes makes it difficult to obtain a good generalization ability for novel samples. To resolve this problem, we provide a scheme that reduces the effective dimensions of data by pruning redundant components for bicategorical classification based on the Bayesian framework. First, the potential of the proposed method is confirmed in ideal situations using the replica method. Unfortunately, performing the scheme exactly is computationally difficult. So, we next develop a tractable approximation algorithm, which turns out to offer nearly optimal performance in ideal cases when the system size is large. Finally, the efficacy of the developed classifier is experimentally examined for a real world problem of colon cancer classification, which shows that the developed method can be practically useful.
ARABIC PART OF SPEECH TAGGING USING K-NEAREST NEIGHBOUR AND NAIVE BAYES CLASSIFIERS COMBINATION
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Rund Mahafdah
2014-01-01
Full Text Available Part Of Speech (POS tagging forms the important preprocessing step in many of the natural language processing applications such as text summarization, question answering and information retrieval system. It is the process of classifying every word in a given context to its appropriate part of speech. Different POS tagging techniques in the literature have been developed and experimented. Currently, it is well known that some POS tagging models are not performing well on the Quranic Arabic due to the complexity of the Quranic Arabic text. This complexity presents several challenges for POS tagging such as high ambiguity, data sparseness and large existence of unknown words. With this in mind, the main problem here is to find out how existing and efficient methods perform in Arabic and how can Quranic corpus be utilized to produce an efficient framework for Arabic POS tagging. We propose a classifiers combination experimental framework for Arabic POS tagger, by selecting two best diverse probabilistic classifiers used in numerous works in non-Arabic language; namely K-Nearest Neighbour (KNN and Naive Bayes (NB. The Majority voting is used here as the combination strategy to exploit classifiers advantages. In addition, an in-depth study has been conducted on a large list of features for exploiting effective features and investigating their role in enhancing the performance of POS taggers for the Quranic Arabic. Hence, this study aims to efficiently integrate different feature sets and tagging algorithms to synthesize more accurate POS tagging procedure. The data used in this study is the Arabic Quranic Corpus, an annotated linguistic resource consisting of 77,430 words with Arabic grammar, syntax and morphology for each word in the Holy Quran. The highest accuracy in the results achieved is 98.32%, which can be a significant enhancement for the state-of-the-art for Arabic Quranic text. The most effective features that yield this accuracy are a
Discriminating complex networks through supervised NDR and Bayesian classifier
Yan, Ke-Sheng; Rong, Li-Li; Yu, Kai
2016-12-01
Discriminating complex networks is a particularly important task for the purpose of the systematic study of networks. In order to discriminate unknown networks exactly, a large set of network measurements are needed to be taken into account for comprehensively considering network properties. However, as we demonstrate in this paper, these measurements are nonlinear correlated with each other in general, resulting in a wide variety of redundant measurements which unintentionally explain the same aspects of network properties. To solve this problem, we adopt supervised nonlinear dimensionality reduction (NDR) to eliminate the nonlinear redundancy and visualize networks in a low-dimensional projection space. Though unsupervised NDR can achieve the same aim, we illustrate that supervised NDR is more appropriate than unsupervised NDR for discrimination task. After that, we perform Bayesian classifier (BC) in the projection space to discriminate the unknown network by considering the projection score vectors as the input of the classifier. We also demonstrate the feasibility and effectivity of this proposed method in six extensive research real networks, ranging from technological to social or biological. Moreover, the effectiveness and advantage of the proposed method is proved by the contrast experiments with the existing method.
Indian Academy of Sciences (India)
Slimane Hameg; Mourad Lazri; Soltane Ameur
2016-07-01
This paper presents a new algorithm to classify convective clouds and determine their intensity, based oncloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI).The convective rainfall events at 15 min, 4 × 5 km spatial resolution from 2006 to 2012 are analysed overnorthern Algeria. The convective rain classification methodology makes use of the relationship betweencloud spectral characteristics and cloud physical properties such as cloud water path (CWP), cloudphase (CP) and cloud top height (CTH). For this classification, a statistical method based on ‘naiveBayes classifier’ is applied. This is a simple probabilistic classifier based on applying ‘Bayes’ theoremwith strong (naive) independent assumptions. For a 9-month period, the ability of SEVIRI to classifythe rainfall intensity in the convective clouds is evaluated using weather radar over the northern Algeria.The results indicate an encouraging performance of the new algorithm for intensity differentiation ofconvective clouds using SEVIRI data.
Evolving a Bayesian Classifier for ECG-based Age Classification in Medical Applications.
Wiggins, M; Saad, A; Litt, B; Vachtsevanos, G
2008-01-01
OBJECTIVE: To classify patients by age based upon information extracted from their electro-cardiograms (ECGs). To develop and compare the performance of Bayesian classifiers. METHODS AND MATERIAL: We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Continuous signal feature variables are converted to a discrete symbolic form by thresholding, to lower the dimensionality of the signal. This simplifies calculation of conditional probability tables for the classifier, and makes the tables smaller. Two methods of network discovery from data were developed and compared: the first using a greedy hill-climb search and the second employed evolutionary computing using a genetic algorithm (GA). RESULTS AND CONCLUSIONS: The evolved Bayesian network performed better (86.25% AUC) than both the one developed using the greedy algorithm (65% AUC) and the naïve Bayesian classifier (84.75% AUC). The methodology for evolving the Bayesian classifier can be used to evolve Bayesian networks in general thereby identifying the dependencies among the variables of interest. Those dependencies are assumed to be non-existent by naïve Bayesian classifiers. Such a classifier can then be used for medical applications for diagnosis and prediction purposes.
Using Unsupervised Learning to Improve the Naive Bayes Classifier for Wireless Sensor Networks
Zwartjes, G.J.; Havinga, Paul J.M.; Smit, Gerardus Johannes Maria; Hurink, Johann L.
2012-01-01
Online processing is essential for many sensor network applications. Sensor nodes can sample far more data than what can practically be transmitted using state of the art sensor network radios. Online processing, however, is complicated due to limited resources of individual nodes. The naive Bayes
Using Unsupervised Learning to Improve the Naive Bayes Classifier for Wireless Sensor Networks
Zwartjes, G.J.; Havinga, Paul J.M.; Smit, Gerardus Johannes Maria; Hurink, Johann L.
2012-01-01
Online processing is essential for many sensor network applications. Sensor nodes can sample far more data than what can practically be transmitted using state of the art sensor network radios. Online processing, however, is complicated due to limited resources of individual nodes. The naive Bayes c
Using Unsupervised Learning to Improve the Naive Bayes Classifier for Wireless Sensor Networks
Zwartjes, G.J.; Havinga, P.J.M.; Smit, G.J.M.; Hurink, J.L.
2012-01-01
Online processing is essential for many sensor network applications. Sensor nodes can sample far more data than what can practically be transmitted using state of the art sensor network radios. Online processing, however, is complicated due to limited resources of individual nodes. The naive Bayes c
Directory of Open Access Journals (Sweden)
Dyarsa Singgih Pamungkas
2015-11-01
Full Text Available Twitter salah satu situs sosial media yang memungkinkan penggunanya untuk menulis tentang berbagai hal yang terjadi dalam sehari-hari. Banyak pengguna mentweet sebuah produk atau layanan yang mereka gunakan. Tweet tersebut dapat digunakan sebagai sumber data untuk menilai sentimen pada Twitter. Pengguna sering menggunakan singkatan kata dan ejaan kata yang salah, dimana dapat menyulitkan fitur yang diambil serta mengurangi ketepatan klasifikasi. Dalam penelitian ini menggunakan Twitter Search API untuk mengambil data dari twitter, penulis menerapkan proses n-gram karakter untuk seleksi fitur serta menggunakan algoritma Naive Bayes Classifier untuk mengklasifikasi sentimen secara otomatis. Penulis menggunakan 3300 data tweet tentang sentimen kepada kata kunci “kurikulum 2013”. Data tersebut diklasifikasi secara manual dan dibagi kedalam masing-masing 1000 data untuk sentimen positif, negatif dan netral. Untuk proses latih di gunakan 3000 data tweet dan 1000 tweet tiap kategori sentimentnya. Hasil penelitian ini menghasilkan sebuah sistem yang dapat mengklasifikasi sentimen secara otomatis dengan hasil pengujian 3000 data latih dan 100 tweet data ujicoba mencapai 91 %. Kata kunci : Twitter, Twitter Search API, sosial media, tweet, analisis sentimen, sentimen, N-gram, Naive Bayes Classifier.
A self-growing Bayesian network classifier for online learning of human motion patterns
Yung, NHC; Chen, Z
2010-01-01
This paper proposes a new self-growing Bayesian network classifier for online learning of human motion patterns (HMPs) in dynamically changing environments. The proposed classifier is designed to represent HMP classes based on a set of historical trajectories labeled by unsupervised clustering. It then assigns HMP class labels to current trajectories. Parameters of the proposed classifier are recalculated based on the augmented dataset of labeled trajectories and all HMP classes are according...
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
Naeini, Mahdi Pakdaman; Cooper, Gregory F; Hauskrecht, Milos
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.
A hybrid classifier using the parallelepiped and Bayesian techniques. [for multispectral image data
Addington, J. D.
1975-01-01
A versatile classification scheme is developed which uses the best features of the parallelepiped algorithm and the Bayesian maximum likelihood algorithm. The parallelepiped technique has the advantage of being very fast, especially when implemented into a table look-up scheme; its disadvantage is its inability to distinguish and classify spectral signatures which are similar in nature. This disadvantage is eliminated by the Bayesian technique which is capable of distinguishing subtle differences very well. The hybrid algorithm developed reduces computer time by as much as 90%. A two- and n-dimensional description of the hybrid classifier is given.
Development of a Prognostic Naive Bayesian Classifier for Successful Treatment of Nonunions
2011-01-25
general anesthesia; if they had an active infection in the brain , spinal cord, or lung tissue or in the treatment area; and/or if they were pregnant...Bethesda, MD 20889 References 1. MacKenzie EJ, Cushing BM, Jurkovich GJ, Morris JA Jr, Burgess AR, deLateur BJ, McAndrew MP, Swiontkowski MF. Physical
The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling
Li, Jingpeng
2008-01-01
Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each persons assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.
基于高斯密度的一阶贝叶斯衍生分类器%First-order Bayesian derivative classifier based on Gaussian density
Institute of Scientific and Technical Information of China (English)
杜瑞杰; 王双成; 高瑞
2015-01-01
针对连续属性朴素贝叶斯分类器不能有效利用属性之间的条件依赖信息，而目前所进行的依赖扩展更关注效率，这使得扩展后分类器的分类准确性还有待提高等问题，使用高斯密度估计属性密度，将属性排序、分类准确性标准与属性父节点的贪婪选择结合，综合考虑效率和分类准确性，对朴素贝叶斯分类器进行依赖扩展，建立一阶贝叶斯衍生分类器，并对属性分类提供的信息进行分析。实验结果显示，基于高斯密度的一阶贝叶斯衍生分类器具有良好的分类准确性。%Naive Bayesian classifier with continuous attributes can not effectively use conditional dependency information be-tween attributes.At present,this paper emphasized the efficiency of its dependency extension,which made the classification accuracy of extended classifier need to be improved.Considering the efficiency and classification accuracy comprehensively and basing on Gaussian function to estimate attribute density,the dependency extension of naive Bayesian classifier with con-tinuous attributes was done by combining attribute sorting,classification accuracy criterion and the greedy selection of attribute parent node to establish first-order Bayesian derivative classifier.It also analyzed the information of attributes providing for class.Experiment results show that first-order Bayesian derivative classifiers have very good classification accuracy.
Directory of Open Access Journals (Sweden)
Paulo Mateus
2013-07-01
Full Text Available We propose a minimum variance unbiased approximation to the conditional relative entropy of the distribution induced by the observed frequency estimates, for multi-classification tasks. Such approximation is an extension of a decomposable scoring criterion, named approximate conditional log-likelihood (aCLL, primarily used for discriminative learning of augmented Bayesian network classifiers. Our contribution is twofold: (i it addresses multi-classification tasks and not only binary-classification ones; and (ii it covers broader stochastic assumptions than uniform distribution over the parameters. Specifically, we considered a Dirichlet distribution over the parameters, which was experimentally shown to be a very good approximation to CLL. In addition, for Bayesian network classifiers, a closed-form equation is found for the parameters that maximize the scoring criterion.
Zyout, Imad; Abdel-Qader, Ikhlas; Jacobs, Christina
2009-01-01
Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spectral, and statistical features of each input mammogram and generates models of real MCs to be used as training samples through a simplified learning phase of the Bayesian classifier. Followed by an estimation of the classifier's decision function parameters, a mammogram is segmented into the identified targets (MCs) against background (healthy tissue). The proposed algorithm has been tested using 23 mammograms from the mini-MIAS database. Experimental results achieved MCs detection with average true positive (sensitivity) and false positive (specificity) of 91.3% and 98.6%, respectively. Results also indicate that the modeling of the real MCs plays a significant role in the performance of the classifier and thus should be given further investigation.
Nowakowska, Marzena
2017-04-01
The development of the Bayesian logistic regression model classifying the road accident severity is discussed. The already exploited informative priors (method of moments, maximum likelihood estimation, and two-stage Bayesian updating), along with the original idea of a Boot prior proposal, are investigated when no expert opinion has been available. In addition, two possible approaches to updating the priors, in the form of unbalanced and balanced training data sets, are presented. The obtained logistic Bayesian models are assessed on the basis of a deviance information criterion (DIC), highest probability density (HPD) intervals, and coefficients of variation estimated for the model parameters. The verification of the model accuracy has been based on sensitivity, specificity and the harmonic mean of sensitivity and specificity, all calculated from a test data set. The models obtained from the balanced training data set have a better classification quality than the ones obtained from the unbalanced training data set. The two-stage Bayesian updating prior model and the Boot prior model, both identified with the use of the balanced training data set, outperform the non-informative, method of moments, and maximum likelihood estimation prior models. It is important to note that one should be careful when interpreting the parameters since different priors can lead to different models.
用于数据采掘的贝叶斯分类器研究%Studies on Bayesian Classifier in Data Mining
Institute of Scientific and Technical Information of China (English)
林士敏; 田凤占; 陆玉昌
2000-01-01
Classification is a basic and important task in data mining and pattern recognising. Bayesian classification is the field of Bayesian learning and Bayesian networks where plenty research works have done and remarkable results have get. In this paiper we discuss the basic concepts of Bayesian classification based on Bayesian learning,the principle and using effects of variant Bayesian classifiers,compare their performances ,and introduce the research advance and further works.
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Eils Roland
2006-06-01
Full Text Available Abstract Background The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction. Results A novel method called HensBC is introduced to predict protein subcellular location. HensBC is a recursive algorithm which constructs a hierarchical ensemble of classifiers. The classifiers used are Bayesian classifiers based on Markov chain models. We tested our method on six various datasets; among them are Gram-negative bacteria dataset, data for discriminating outer membrane proteins and apoptosis proteins dataset. We observed that our method can predict the subcellular location with high accuracy. Another advantage of the proposed method is that it can improve the accuracy of the prediction of some classes with few sequences in training and is therefore useful for datasets with imbalanced distribution of classes. Conclusion This study introduces an algorithm which uses only the primary sequence of a protein to predict its subcellular location. The proposed recursive scheme represents an interesting methodology for learning and combining classifiers. The method is computationally efficient and competitive with the previously reported approaches in terms of prediction accuracies as empirical results indicate. The code for the software is available upon request.
Doyle, Scott; Feldman, Michael; Tomaszewski, John; Madabhushi, Anant
2012-05-01
Diagnosis of prostate cancer (CaP) currently involves examining tissue samples for CaP presence and extent via a microscope, a time-consuming and subjective process. With the advent of digital pathology, computer-aided algorithms can now be applied to disease detection on digitized glass slides. The size of these digitized histology images (hundreds of millions of pixels) presents a formidable challenge for any computerized image analysis program. In this paper, we present a boosted Bayesian multiresolution (BBMR) system to identify regions of CaP on digital biopsy slides. Such a system would serve as an important preceding step to a Gleason grading algorithm, where the objective would be to score the invasiveness and severity of the disease. In the first step, our algorithm decomposes the whole-slide image into an image pyramid comprising multiple resolution levels. Regions identified as cancer via a Bayesian classifier at lower resolution levels are subsequently examined in greater detail at higher resolution levels, thereby allowing for rapid and efficient analysis of large images. At each resolution level, ten image features are chosen from a pool of over 900 first-order statistical, second-order co-occurrence, and Gabor filter features using an AdaBoost ensemble method. The BBMR scheme, operating on 100 images obtained from 58 patients, yielded: 1) areas under the receiver operating characteristic curve (AUC) of 0.84, 0.83, and 0.76, respectively, at the lowest, intermediate, and highest resolution levels and 2) an eightfold savings in terms of computational time compared to running the algorithm directly at full (highest) resolution. The BBMR model outperformed (in terms of AUC): 1) individual features (no ensemble) and 2) a random forest classifier ensemble obtained by bagging multiple decision tree classifiers. The apparent drop-off in AUC at higher image resolutions is due to lack of fine detail in the expert annotation of CaP and is not an artifact of the
Guitarist Fingertip Tracking by Integrating a Bayesian Classifier into Particle Filters
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Chutisant Kerdvibulvech
2008-01-01
Full Text Available We propose a vision-based method for tracking guitar fingerings made by guitar players. We present it as a new framework for tracking colored finger markers by integrating a Bayesian classifier into particle filters. This adds the useful abilities of automatic track initialization and recovery from tracking failures in a dynamic background. Furthermore, by using the online adaptation of color probabilities, this method is able to cope with illumination changes. Augmented Reality Tag (ARTag is then utilized to calculate the projection matrix as an online process which allows the guitar to be moved while being played. Representative experimental results are also included. The method presented can be used to develop the application of human-computer interaction (HCI to guitar playing by recognizing the chord being played by a guitarist in virtual spaces. The aforementioned application would assist guitar learners by allowing them to automatically identify if they are using the correct chords required by the musical piece.
Nicandro, Cruz-Ramírez; Efrén, Mezura-Montes; María Yaneli, Ameca-Alducin; Enrique, Martín-Del-Campo-Mena; Héctor Gabriel, Acosta-Mesa; Nancy, Pérez-Castro; Alejandro, Guerra-Hernández; Guillermo de Jesús, Hoyos-Rivera; Rocío Erandi, Barrientos-Martínez
2013-01-01
Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool. PMID:23762182
Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier
Luqman, Muhammad Muzzamil; Ramel, Jean-Yves
2010-01-01
We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates.
Sinha, Shriprakash
2015-07-01
In this manuscript the reproducibility of parameter learning with missing observations in a naive Bayesian network and its effect on the prediction results for Wnt signaling activation in colorectal cancer is tested. The training of the network is carried out separately on doxycycline-treated LS174T cell lines (GSE18560) as well as normal and adenoma samples (GSE8671). A computational framework to test the reproducibility of the parameters is designed in order check the veracity of the prediction results. Detailed experimental analysis suggests that the prediction results are accurate and reproducible with negligible deviations. Anomalies in estimated parameters are accounted for due to the representation issues of the Bayesian network model. High prediction accuracies are reported for normal (N) and colon-related adenomas (AD), colorectal cancer (CRC), carcinomas (C), adenocarcinomas (ADC) and replication error colorectal cancer (RER CRC) test samples. Test samples from inflammatory bowel diseases (IBD) do not fare well in the prediction test. Also, an interesting case regarding hypothesis testing came up while proving the statistical significance of the different design setups of the Bayesian network model. It was found that hypothesis testing may not be the correct way to check the significance between design setups, especially when the structure of the model is the same, given that the model is trained on a single piece of test data. The significance test does have value when the datasets are independent. Finally, in comparison to the biologically inspired models, the naive Bayesian model may give accurate results, but this accuracy comes at the cost of a loss of crucial biological knowledge which might help reveal hidden relations among intra/extracellular factors affecting the Wnt pathway.
Bulashevska, Alla; Stein, Martin; Jackson, David; Eils, Roland
2009-12-01
Accurate computational methods that can help to predict biological function of a protein from its sequence are of great interest to research biologists and pharmaceutical companies. One approach to assume the function of proteins is to predict the interactions between proteins and other molecules. In this work, we propose a machine learning method that uses a primary sequence of a domain to predict its propensity for interaction with small molecules. By curating the Pfam database with respect to the small molecule binding ability of its component domains, we have constructed a dataset of small molecule binding and non-binding domains. This dataset was then used as training set to learn a Bayesian classifier, which should distinguish members of each class. The domain sequences of both classes are modelled with Markov chains. In a Jack-knife test, our classification procedure achieved the predictive accuracies of 77.2% and 66.7% for binding and non-binding classes respectively. We demonstrate the applicability of our classifier by using it to identify previously unknown small molecule binding domains. Our predictions are available as supplementary material and can provide very useful information to drug discovery specialists. Given the ubiquitous and essential role small molecules play in biological processes, our method is important for identifying pharmaceutically relevant components of complete proteomes. The software is available from the author upon request.
Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas
Kozoderov, Vladimir; Kondranin, Timofei; Dmitriev, Egor; Kamentsev, Vladimir
2015-06-01
Pattern recognition problem is outlined in the context of textural and spectral analysis of remote sensing imagery processing. Main attention is paid to Bayesian classifier that can be used to realize the processing procedures based on parallel machine-learning algorithms and high-productive computers. We consider the maximum of the posterior probability principle and the formalism of Markov random fields for the neighborhood description of the pixels for the related classes of objects with the emphasis on forests of different species and ages. The energy category of the selected classes serves to account for the likelihood measure between the registered radiances and the theoretical distribution functions approximating remotely sensed data. Optimization procedures are undertaken to solve the pattern recognition problem of the texture description for the forest classes together with finding thin nuances of their spectral distribution in the feature space. As a result, possible redundancy of the channels for imaging spectrometer due to their correlations is removed. Difficulties are revealed due to different sampling data while separating pixels, which characterize the sunlit tops, shaded space and intermediate cases of the Sun illumination conditions on the hyperspectral images. Such separation of pixels for the forest classes is maintained to enhance the recognition accuracy, but learning ensembles of data need to be agreed for these categories of pixels. We present some results of the Bayesian classifier applicability for recognizing airborne hyperspectral images using the relevant improvements in separating such pixels for the forest classes on a test area of the 4 × 10 km size encompassed by 13 airborne tracks, each forming the images by 500 pixels across the track and from 10,000 to 14,000 pixels along the track. The spatial resolution of each image is near to 1 m from the altitude near to 2 km above the ground level. The results of the hyperspectral imagery
Energy Technology Data Exchange (ETDEWEB)
Benndorf, Matthias; Kotter, Elmar; Langer, Mathias [University Hospital Freiburg, Department of Radiology, Freiburg (Germany); Herda, Christoph [Kantonsspital Graubuenden, Chur (Switzerland); Wu, Yirong; Burnside, Elizabeth S. [University of Wisconsin-Madison School of Medicine and Public Health, Department of Radiology, Madison, WI (United States)
2015-06-01
To develop and validate a decision support tool for mammographic mass lesions based on a standardized descriptor terminology (BI-RADS lexicon) to reduce variability of practice. We used separate training data (1,276 lesions, 138 malignant) and validation data (1,177 lesions, 175 malignant). We created naive Bayes (NB) classifiers from the training data with tenfold cross-validation. Our ''inclusive model'' comprised BI-RADS categories, BI-RADS descriptors, and age as predictive variables; our ''descriptor model'' comprised BI-RADS descriptors and age. The resulting NB classifiers were applied to the validation data. We evaluated and compared classifier performance with ROC-analysis. In the training data, the inclusive model yields an AUC of 0.959; the descriptor model yields an AUC of 0.910 (P < 0.001). The inclusive model is superior to the clinical performance (BI-RADS categories alone, P < 0.001); the descriptor model performs similarly. When applied to the validation data, the inclusive model yields an AUC of 0.935; the descriptor model yields an AUC of 0.876 (P < 0.001). Again, the inclusive model is superior to the clinical performance (P < 0.001); the descriptor model performs similarly. We consider our classifier a step towards a more uniform interpretation of combinations of BI-RADS descriptors. We provide our classifier at www.ebm-radiology.com/nbmm/index.html. (orig.)
Institute of Scientific and Technical Information of China (English)
苏宏升
2008-01-01
To make conventional Bayesian optimal classifier possess the abilities of disposing fuzzy information and realizing the automation of reasoning process, a new Bayesian optimal classifier is proposed with fuzzy information embedded. It can not only dispose fuzzy information effectively, but also retain learning properties of Bayesian optimal classifier. In addition, according to the evolution of fuzzy set theory, vague set is also imbedded into it to generate vague Bayesian optimal classifier. It can simultaneously simulate the twofold characteristics of fuzzy information from the positive and reverse directions. Further, a set pair Bayesian optimal classifier is also proposed considering the threefold characteristics of fuzzy information from the positive, reverse, and indeterminate sides. In the end, a knowledge-based artificial neural network (KBANN) is presented to realize automatic reasoning of Bayesian optimal classifier. It not only reduces the computational cost of Bayesian optimal classifier but also improves its classification learning quality.
Institute of Scientific and Technical Information of China (English)
吴陈; 王万川
2012-01-01
In light of the problems in direct marketing the retail chain enterprises have, this paper present the naive Bayesian classification model based on EM clustering according to model mining and selection method of prototype theory. Through the experiment, it proves that this model clearly outperforms the K-means clustering naive Bayesian model and no-clustering naive Bayesian model in performance of customers purchasing behaviour prediction. At last, this model is also used in checking up the effectiveness of classification prediction conducted on new customers in direct marketing.%针对连锁型零售企业直接营销中的问题,基于原型理论的挖掘模型选择方法,提出基于EM聚类朴素贝叶斯模型,通过实验证明了该模型在客户购买行为的预测性能上明显优于基于K-means聚类朴素贝叶斯模型和无聚类的朴素贝叶斯模型.最后,利用该模型检验了直接营销中的对新客户进行分类预测的有效性.
Statistical analysis of a Bayesian classifier based on the expression of miRNAs
Ricci, Leonardo; Del Vescovo, Valerio; Cantaloni, Chiara; Grasso, Margherita; Barbareschi, Mattia; Denti, Michela Alessandra
2015-01-01
Background During the last decade, many scientific works have concerned the possible use of miRNA levels as diagnostic and prognostic tools for different kinds of cancer. The development of reliable classifiers requires tackling several crucial aspects, some of which have been widely overlooked in the scientific literature: the distribution of the measured miRNA expressions and the statistical uncertainty that affects the parameters that characterize a classifier. In this paper, these topics ...
Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier
Luqman, Muhammad Muzzamil; Brouard, Thierry; Ramel, Jean-Yves
2010-01-01
We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational g...
Fytilis, N.; Rizzo, D. M.
2012-12-01
Environmental managers are increasingly required to forecast the long-term effects and the resilience or vulnerability of biophysical systems to human-generated stresses. Mitigation strategies for hydrological and environmental systems need to be assessed in the presence of uncertainty. An important aspect of such complex systems is the assessment of variable uncertainty on the model response outputs. We develop a new classification tool that couples a Naïve Bayesian Classifier with a modified Kohonen Self-Organizing Map to tackle this challenge. For proof-of-concept, we use rapid geomorphic and reach-scale habitat assessments data from over 2500 Vermont stream reaches (~1371 stream miles) assessed by the Vermont Agency of Natural Resources (VTANR). In addition, the Vermont Department of Environmental Conservation (VTDEC) estimates stream habitat biodiversity indices (macro-invertebrates and fish) and a variety of water quality data. Our approach fully utilizes the existing VTANR and VTDEC data sets to improve classification of stream-reach habitat and biological integrity. The combined SOM-Naïve Bayesian architecture is sufficiently flexible to allow for continual updates and increased accuracy associated with acquiring new data. The Kohonen Self-Organizing Map (SOM) is an unsupervised artificial neural network that autonomously analyzes properties inherent in a given a set of data. It is typically used to cluster data vectors into similar categories when a priori classes do not exist. The ability of the SOM to convert nonlinear, high dimensional data to some user-defined lower dimension and mine large amounts of data types (i.e., discrete or continuous, biological or geomorphic data) makes it ideal for characterizing the sensitivity of river networks in a variety of contexts. The procedure is data-driven, and therefore does not require the development of site-specific, process-based classification stream models, or sets of if-then-else rules associated with
Directory of Open Access Journals (Sweden)
V. Miskovic
2017-02-01
Full Text Available This paper presents an implementation of a mobile application that provides a real-time personalized assessment of patient’s activities by using a Flexible Bayesian Classifier. The personalized assessment is derived from data collected from the 3-axial accelerometer sensor and the counting steps sensor, both widespread among nowadays mobile devices. Despite the fact that online mobile solutions with Bayesian Classifier have been rare and insufficiently precise, we have proven that the accuracy of the proposed system within a defined data model is comparable to the accuracy of decision trees and neural networks.
Directory of Open Access Journals (Sweden)
Wu Steven H
2012-06-01
Full Text Available Abstract Background Two-dimensional polyacrylamide gel electrophoresis (2D PAGE is commonly used to identify differentially expressed proteins under two or more experimental or observational conditions. Wu et al (2009 developed a univariate probabilistic model which was used to identify differential expression between Case and Control groups, by applying a Likelihood Ratio Test (LRT to each protein on a 2D PAGE. In contrast to commonly used statistical approaches, this model takes into account the two possible causes of missing values in 2D PAGE: either (1 the non-expression of a protein; or (2 a level of expression that falls below the limit of detection. Results We develop a global Bayesian model which extends the previously described model. Unlike the univariate approach, the model reported here is able treat all differentially expressed proteins simultaneously. Whereas each protein is modelled by the univariate likelihood function previously described, several global distributions are used to model the underlying relationship between the parameters associated with individual proteins. These global distributions are able to combine information from each protein to give more accurate estimates of the true parameters. In our implementation of the procedure, all parameters are recovered by Markov chain Monte Carlo (MCMC integration. The 95% highest posterior density (HPD intervals for the marginal posterior distributions are used to determine whether differences in protein expression are due to differences in mean expression intensities, and/or differences in the probabilities of expression. Conclusions Simulation analyses showed that the global model is able to accurately recover the underlying global distributions, and identify more differentially expressed proteins than the simple application of a LRT. Additionally, simulations also indicate that the probability of incorrectly identifying a protein as differentially expressed (i.e., the False
3-Layered Bayesian Model Using in Text Classification
Directory of Open Access Journals (Sweden)
Chang Jiayu
2013-01-01
Full Text Available Naive Bayesian is one of quite effective classification methods in all of the text disaggregated models. Usually, the computed result will be large deviation from normal, with the reason of attribute relevance and so on. This study embarked from the degree of correlation, defined the node’s degree as well as the relations between nodes, proposed a 3-layered Bayesian Model. According to the conditional probability recurrence formula, the theory support of the 3-layered Bayesian Model is obtained. According to the theory analysis and the empirical datum contrast to the Naive Bayesian, the model has better attribute collection and classify. It can be also promoted to the Multi-layer Bayesian Model using in text classification.
Verma, Sneha K.; Chun, Sophia; Liu, Brent J.
2014-03-01
Pain is a common complication after spinal cord injury with prevalence estimates ranging 77% to 81%, which highly affects a patient's lifestyle and well-being. In the current clinical setting paper-based forms are used to classify pain correctly, however, the accuracy of diagnoses and optimal management of pain largely depend on the expert reviewer, which in many cases is not possible because of very few experts in this field. The need for a clinical decision support system that can be used by expert and non-expert clinicians has been cited in literature, but such a system has not been developed. We have designed and developed a stand-alone tool for correctly classifying pain type in spinal cord injury (SCI) patients, using Bayesian decision theory. Various machine learning simulation methods are used to verify the algorithm using a pilot study data set, which consists of 48 patients data set. The data set consists of the paper-based forms, collected at Long Beach VA clinic with pain classification done by expert in the field. Using the WEKA as the machine learning tool we have tested on the 48 patient dataset that the hypothesis that attributes collected on the forms and the pain location marked by patients have very significant impact on the pain type classification. This tool will be integrated with an imaging informatics system to support a clinical study that will test the effectiveness of using Proton Beam radiotherapy for treating spinal cord injury (SCI) related neuropathic pain as an alternative to invasive surgical lesioning.
基于贝叶斯分类的主题爬虫%Fcoused crawler bused on Bayesian classifier
Institute of Scientific and Technical Information of China (English)
贾海军; 陈海光
2013-01-01
随着网络的高速发展，其信息资源越来越庞大，面对巨量的信息库，搜索引擎起着重要的作用。主题爬虫技术作为搜索引擎的主要核心部分，计算搜索结果与搜索主题的关系，该关系被称为相关性。一般主题爬虫方法只计算网页内容与搜索主题的相关性，作者所提主题爬虫，通过链接内容和锚文本内容计算链接的重要性，然后利用贝叶斯分类器对链接进行分类，最后利用余弦相似函数计算网页的相关性，如果相关值大于阀值，则认为该网页与预定主题相关，否则不相关。实验结果证明：所提出主题爬虫方法可以获得很高的精确度。%With the rapid development of the network,its information resources are increasingly large and faced a huge amount of information database, search engine plays an important role. Focused crawling technique, as the main core portion of search engine,is used to calculate the relationship between search results and search topics,which is called correlation. Normally,focused crawling method is used only to calculate the correlation between web content and search related topics. In this paper, focused crawling method is used to compute the importance of links through link content and anchor text,then Bayesian classifier is used to classify the links,and finally cosine similarity function is used to calculate the relevance of web pages. If the correlation value is greater than the threshold the page is considered to be associated with the predetermined topics, otherwise not relevant. Experimental results show that a high accuracy can be obtained by using the proposed crawling approach.
基于朴素贝叶斯与ID3算法的决策树分类%Decision Tree Classification Based on Naive Bayesian and ID3 Algorithm
Institute of Scientific and Technical Information of China (English)
黄宇达; 王迤冉
2012-01-01
在朴素贝叶斯算法和ID3算法的基础上,提出一种改进的决策树分类算法.引入客观属性重要度参数,给出弱化的朴素贝叶斯条件独立性假设,并采用加权独立信息熵作为分类属性的选取标准.理论分析和实验结果表明,改进算法能在一定程度上克服ID3算法的多值偏向问题,并且具有较高的执行效率和分类准确度.%This paper proposes an improved decision tree classification algorithm based on naive Bayes algorithm and ID3 algorithm. It introduces objective attribute importance parameter, gives a kind of conditional independence assumption that is weaker than naive Bayesian algorithm, and uses the weighted independent information entropy as splitting attribute's selection criteria. Theoretical analysis and experimental results show that the improved algorithm, to a certain extent well overcomes ID3 algorithm's shortcoming of multi-value tendency, and improves algorithm's implementation efficiency and classification accuracy.
Ranking Features on Psychological Dynamics of Cooperative Team Work through Bayesian Networks
Pilar Fuster-Parra; Alex García-Mas; Jaume Cantallops; F. Javier Ponseti; Yuhua Luo
2016-01-01
The aim of this study is to rank some features that characterize the psychological dynamics of cooperative team work in order to determine priorities for interventions and formation: leading positive feedback, cooperative manager and collaborative manager features. From a dataset of 20 cooperative sport teams (403 soccer players), the characteristics of the prototypical sports teams are studied using an average Bayesian network (BN) and two special types of BNs, the Bayesian classifiers: naiv...
基于共轭先验分布的贝叶斯网络分类模型%Bayesian network classifier based on conjugate prior distribution
Institute of Scientific and Technical Information of China (English)
杨颖涛; 王跃钢; 邓卫强; 徐洪涛
2012-01-01
针对贝叶斯网络后验概率需计算样本边际分布,计算代价大的问题,将共轭先验分布思想引入贝叶斯分类,提出了基于共轭先验分布的贝叶斯网络分类模型.针对非区间离散样本,提出一种自适应的样本离散方法,将小波包提取模拟电路故障特征离散化作为分类模型属性.仿真验证表明,模型分类效果较好,算法运行速度得以提高,也可应用于连续样本和多分类的情况,扩展了贝叶斯网络分类的应用范围.%In order to reducing calculate costs of Bayesian network,when calculating posterior probability of samples that need the marginal distribution,an approach of Bayesian network classifier based on conjugate prior distribution is proposed. An adaptive discretization method is also proposed to discrete non-interval samples. The fault feature of analog circuit extracted by wavelet packet is taken as a discrete property of Bayesian network classification model. The simulation result shows that,this classifier has high accuracy and efficiency of analog circuit fault diagnosis,and can be applied to continuous and multi-classification case,which extends the scope of application of Bayesian network classification.
Bayesian Network Classifier Based on L1 Regularization%基于L1正则化的贝叶斯网络分类器
Institute of Scientific and Technical Information of China (English)
王影; 王浩; 俞奎; 姚宏亮
2012-01-01
Variable order-based Bayesian network classifiers ignore the information of the selected variables in their sequence and their class label, which significantly hurts the classification accuracy. To address this problem, we proposed a simple and efficient LI regularized Bayesian network classifier (Ll-BNC). Through adjusting the constraint value of Lasso and fully taking advantage of the regression residuals of the information, Ll-BNC takes the information of the sequence of selected variables and the class label into account,and then generates an excellent variable ordering sequence (LI regularization path) for constructing a good Bayesian network classifier by the K2 algorithm. Experimental results show that Ll-BNC outperforms existing state-of-the-art Bayesian network classifiers. In addition, in comparison with SVM.Knn and J48 classification algorithms,Ll-BNC is also superior to those algorithms on most datasets.%目前基于节点排序的贝叶斯网络分类器忽略了节点序列中已选变量和类标签之间的信息,导致分类器的准确率很难进一步提高.针对这个问题,提出了一种简单高效的贝叶斯网络分类器的学习算法:L1正则化的贝叶斯网络分类器(L1-BNC).通过调整Lasso方法中的约束值,充分利用回归残差的信息,结合点序列中已选变量和类标签的信息,形成一条优秀的有序变量拓扑序列(L1正则化路径)；基于该序列,利用K2算法生成优良的贝叶斯网络分类器.实验表明,L1-BNC在分类精度上优于已有的贝叶斯网络分类器.L1-BNC也与SVM,KNN和J48分类算法进行了比较,在大部分数据集上,L1-BNC优于这些算法.
Directory of Open Access Journals (Sweden)
Nedunchelian Ramanujam
2016-01-01
Full Text Available Nowadays, automatic multidocument text summarization systems can successfully retrieve the summary sentences from the input documents. But, it has many limitations such as inaccurate extraction to essential sentences, low coverage, poor coherence among the sentences, and redundancy. This paper introduces a new concept of timestamp approach with Naïve Bayesian Classification approach for multidocument text summarization. The timestamp provides the summary an ordered look, which achieves the coherent looking summary. It extracts the more relevant information from the multiple documents. Here, scoring strategy is also used to calculate the score for the words to obtain the word frequency. The higher linguistic quality is estimated in terms of readability and comprehensibility. In order to show the efficiency of the proposed method, this paper presents the comparison between the proposed methods with the existing MEAD algorithm. The timestamp procedure is also applied on the MEAD algorithm and the results are examined with the proposed method. The results show that the proposed method results in lesser time than the existing MEAD algorithm to execute the summarization process. Moreover, the proposed method results in better precision, recall, and F-score than the existing clustering with lexical chaining approach.
Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection
Farid, Dewan Md; Rahman, Mohammad Zahidur; 10.5121/ijnsa.2010.2202
2010-01-01
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learn...
Bayesian cloud detection for MERIS, AATSR, and their combination
Directory of Open Access Journals (Sweden)
A. Hollstein
2014-11-01
Full Text Available A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS, the Advanced Along-Track Scanning Radiometer (AATSR, and their combination. The cloud masks were designed to be numerically efficient and suited for the processing of large amounts of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient amounts of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.
Gautier, Mathieu
2014-11-01
The recent democratization of next-generation-sequencing-based approaches towards nonmodel species has made it cost-effective to produce large genotyping data sets for a wider range of species. However, when no detailed genome assembly is available, poor knowledge about the organization of the markers within the genome might hamper the optimal use of this abundant information. At the most basic level of genomic organization, the type of chromosome (autosomes, sex chromosomes, mitochondria or chloroplast in plants) may remain unknown for most markers which might be limiting or even misleading in some applications, particularly in population genetics. Conversely, the characterization of sex-linked markers allows molecular sexing of the individuals. In this study, we propose a Bayesian model-based classifier named detsex, to assign markers to their chromosome type and/or to perform sexing of individuals based on genotyping data. The performance of detsex is further evaluated by a comprehensive simulation study and by the analysis of real data sets from various origins (microsatellite and SNP data derived from genotyping assay designs and NGS experiments). Irrespective of the origin of the markers or the size of the data set, detsex was proved efficient (i) to identify the sex-linked markers, (ii) to perform molecular sexing of the individuals and (iii) to perform basic quality check of the genotyping data sets. The underlying structure of the model also allows to consider each of these potential applications either separately or jointly.
Bayesian network modelling of upper gastrointestinal bleeding
Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri
2013-09-01
Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.
Gender recognition using local binary pattern and Naive Bayes ...
African Journals Online (AJOL)
Gender recognition using local binary pattern and Naive Bayes Classifier. ... identity authentication, access control and human-computer interaction amongst others. Gender recognition is a fundamental task for human beings, as many social ...
Directory of Open Access Journals (Sweden)
Marcelo de Carvalho Griebeler
2015-09-01
Full Text Available There has been in some countries a trend of assigning other functions to central banks besides price stability. The most suggested function to be added to monetary authority’s obligations is to pursue economic growth or full employment. In this paper we characterize the behavior and analyse the optimal monetary policy of, what we call, a naive central banker. We describe the naive behavior as one that does face the inflation-unemployment trade-off, but it tries to minimize both variables simultaneously. Our findings, both under discretion and commitment, indicate that the naive central banker delivers lower expected inflation and inflation variance than the benchmark behavior whenever the economy is rigid enough. However, the degree of conservativeness also affects this result, such that the less conservative the naive policymaker, the more rigidity is necessary.
Textual and visual content-based anti-phishing: a Bayesian approach.
Zhang, Haijun; Liu, Gang; Chow, Tommy W S; Liu, Wenyin
2011-10-01
A novel framework using a Bayesian approach for content-based phishing web page detection is presented. Our model takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages. A text classifier, an image classifier, and an algorithm fusing the results from classifiers are introduced. An outstanding feature of this paper is the exploration of a Bayesian model to estimate the matching threshold. This is required in the classifier for determining the class of the web page and identifying whether the web page is phishing or not. In the text classifier, the naive Bayes rule is used to calculate the probability that a web page is phishing. In the image classifier, the earth mover's distance is employed to measure the visual similarity, and our Bayesian model is designed to determine the threshold. In the data fusion algorithm, the Bayes theory is used to synthesize the classification results from textual and visual content. The effectiveness of our proposed approach was examined in a large-scale dataset collected from real phishing cases. Experimental results demonstrated that the text classifier and the image classifier we designed deliver promising results, the fusion algorithm outperforms either of the individual classifiers, and our model can be adapted to different phishing cases.
Bayesian artificial intelligence
Korb, Kevin B
2010-01-01
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente
The fuzzy gene filter: A classifier performance assesment
Perez, Meir
2011-01-01
The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed to rank genes in order of differential expression, based on expression data generated in a microarray experiment. This paper examines the effectiveness of the FGF for feature selection using various classification architectures. The FGF is compared to three of the most common gene ranking algorithms: t-test, Wilcoxon test and ROC curve analysis. Four classification schemes are used to compare the performance of the FGF vis-a-vis the standard approaches: K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and Artificial Neural Network (ANN). A nested stratified Leave-One-Out Cross Validation scheme is used to identify the optimal number top ranking genes, as well as the optimal classifier parameters. Two microarray data sets are used for the comparison: a prostate cancer data set and a lymphoma data set.
Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries.
Zelic, I; Kononenko, I; Lavrac, N; Vuga, V
1997-12-01
Machine learning techniques can be used to extract knowledge from data stored in medical databases. In our application, various machine learning algorithms were used to extract diagnostic knowledge which may be used to support the diagnosis of sport injuries. The applied methods include variants of the Assistant algorithm for top-down induction of decision trees, and variants of the Bayesian classifier. The available dataset was insufficient for reliable diagnosis of all sport injuries considered by the system. Consequently, expert-defined diagnostic rules were added and used as pre-classifiers or as generators of additional training instances for diagnoses for which only few training examples were available. Experimental results show that the classification accuracy and the explanation capability of the naive Bayesian classifier with the fuzzy discretization of numerical attributes were superior to other methods and estimated as the most appropriate for practical use.
Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection
Directory of Open Access Journals (Sweden)
Dewan Md. Farid
2010-04-01
Full Text Available In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposedalgorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS. We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR andsignificant reduce false positives (FP for different types of network intrusions using limited computational resources
Institute of Scientific and Technical Information of China (English)
彭兴媛; 刘琼荪; 王立威
2011-01-01
采用条件互信息来度量任意2个条件属性之间的关联程度,采用互信息度量各条件属性与类属性间的关联程度,以此作为将各条件属性进行聚类的准则,提出一种新的将条件属性进行聚类的分组技术.同时,结合朴素贝叶斯分类算法,构造了改进的朴素贝叶斯分类模型.通过仿真实验表明该文提出的算法具有较好的分类性能.%In this paper,the correlation intensity of two arbitrary conditional attributes was measured by conditional mutual information,and the correlation intensity between every conditional attribute and classification attribute was measured by mutual information.On that criterion to cluster the conditional attributes,a new grouping method to cluster the conditional attributes was proposed.Simultaneously,combined with naive bayes classification algorithm,an improved naive bayes classification model was constructed.Simulation results showed the efficiency of this method is preferable.
Daumer, Martin; Dürr, Detlef; Goldstein, Sheldon; Zanghì, Nino
1996-01-01
A source of much difficulty and confusion in the interpretation of quantum mechanics is a ``naive realism about operators.'' By this we refer to various ways of taking too seriously the notion of operator-as-observable, and in particular to the all too casual talk about ``measuring operators'' that occurs when the subject is quantum mechanics. Without a specification of what should be meant by ``measuring'' a quantum observable, such an expression can have no clear meaning. A definite specifi...
Institute of Scientific and Technical Information of China (English)
耿兰芹; 王芳; 赵文清
2006-01-01
针对传统变压器故障诊断中的对于原始测试数据完备性和准确性的限制,提出并构建用于变压器故障诊断的NB模型;并在此基础上针对NB模型缺失关键属性时诊断性能大大降低的弱点,提出用SVM回归法填补缺失属性,构建SVM回归预测与NB相结合的变压器故障诊断模型.实验表明在属性缺失多及缺失关键属性时,SVM回归预测的应用能够明显提高诊断的正判率.
Mallatt, Jon M; Garey, James R; Shultz, Jeffrey W
2004-04-01
Relationships among the ecdysozoans, or molting animals, have been difficult to resolve. Here, we use nearly complete 28S+18S ribosomal RNA gene sequences to estimate the relations of 35 ecdysozoan taxa, including newly obtained 28S sequences from 25 of these. The tree-building algorithms were likelihood-based Bayesian inference and minimum-evolution analysis of LogDet-transformed distances, and hypotheses were tested wth parametric bootstrapping. Better taxonomic resolution and recovery of established taxa were obtained here, especially with Bayesian inference, than in previous parsimony-based studies that used 18S rRNA sequences (or 18S plus small parts of 28S). In our gene trees, priapulan worms represent the basal ecdysozoans, followed by nematomorphs, or nematomorphs plus nematodes, followed by Panarthropoda. Panarthropoda was monophyletic with high support, although the relationships among its three phyla (arthropods, onychophorans, tardigrades) remain uncertain. The four groups of arthropods-hexapods (insects and related forms), crustaceans, chelicerates (spiders, scorpions, horseshoe crabs), and myriapods (centipedes, millipedes, and relatives)-formed two well-supported clades: Hexapoda in a paraphyletic crustacea (Pancrustacea), and 'Chelicerata+Myriapoda' (a clade that we name 'Paradoxopoda'). Pycnogonids (sea spiders) were either chelicerates or part of the 'chelicerate+myriapod' clade, but not basal arthropods. Certain clades derived from morphological taxonomy, such as Mandibulata, Atelocerata, Schizoramia, Maxillopoda and Cycloneuralia, are inconsistent with these rRNA data. The 28S gene contained more signal than the 18S gene, and contributed to the improved phylogenetic resolution. Our findings are similar to those obtained from mitochondrial and nuclear (e.g., elongation factor, RNA polymerase, Hox) protein-encoding genes, and should revive interest in using rRNA genes to study arthropod and ecdysozoan relationships.
Daumer, M; Goldstein, S; Zanghì, N; Daumer, Martin; Durr, Detlef; Goldstein, Sheldon; Zangh, Nino
1996-01-01
A source of much difficulty and confusion in the interpretation of quantum mechanics is a ``naive realism about operators.'' By this we refer to various ways of taking too seriously the notion of operator-as-observable, and in particular to the all too casual talk about ``measuring operators'' that occurs when the subject is quantum mechanics. Without a specification of what should be meant by ``measuring'' a quantum observable, such an expression can have no clear meaning. A definite specification is provided by Bohmian mechanics, a theory that emerges from Sch\\"rodinger's equation for a system of particles when we merely insist that ``particles'' means particles. Bohmian mechanics clarifies the status and the role of operators as observables in quantum mechanics by providing the operational details absent from standard quantum mechanics. It thereby allows us to readily dismiss all the radical claims traditionally enveloping the transition from the classical to the quantum realm---for example, that we must a...
The Persistence of "Solid" and "Liquid" Naive Conceptions: A Reaction Time Study
Babai, Reuven; Amsterdamer, Anat
2008-01-01
The study explores whether the naive concepts of "solid" and "liquid" persist in adolescence. Accuracy of responses and reaction times where measured while 41 ninth graders classified different solids (rigid, non-rigid and powders) and different liquids (runny, dense) into solid or liquid. The results show that these naive conceptions affect…
Pumpe, Daniel; Greiner, Maksim; Müller, Ewald; Enßlin, Torsten A.
2016-07-01
Stochastic differential equations describe well many physical, biological, and sociological systems, despite the simplification often made in their derivation. Here the usage of simple stochastic differential equations to characterize and classify complex dynamical systems is proposed within a Bayesian framework. To this end, we develop a dynamic system classifier (DSC). The DSC first abstracts training data of a system in terms of time-dependent coefficients of the descriptive stochastic differential equation. Thereby the DSC identifies unique correlation structures within the training data. For definiteness we restrict the presentation of the DSC to oscillation processes with a time-dependent frequency ω (t ) and damping factor γ (t ) . Although real systems might be more complex, this simple oscillator captures many characteristic features. The ω and γ time lines represent the abstract system characterization and permit the construction of efficient signal classifiers. Numerical experiments show that such classifiers perform well even in the low signal-to-noise regime.
YAP Induces Human Naive Pluripotency
Directory of Open Access Journals (Sweden)
Han Qin
2016-03-01
Full Text Available The human naive pluripotent stem cell (PSC state, corresponding to a pre-implantation stage of development, has been difficult to capture and sustain in vitro. We report that the Hippo pathway effector YAP is nuclearly localized in the inner cell mass of human blastocysts. Overexpression of YAP in human embryonic stem cells (ESCs and induced PSCs (iPSCs promotes the generation of naive PSCs. Lysophosphatidic acid (LPA can partially substitute for YAP to generate transgene-free human naive PSCs. YAP- or LPA-induced naive PSCs have a rapid clonal growth rate, a normal karyotype, the ability to form teratomas, transcriptional similarities to human pre-implantation embryos, reduced heterochromatin levels, and other hallmarks of the naive state. YAP/LPA act in part by suppressing differentiation-inducing effects of GSK3 inhibition. CRISPR/Cas9-generated YAP−/− cells have an impaired ability to form colonies in naive but not primed conditions. These results uncover an unexpected role for YAP in the human naive state, with implications for early human embryology.
Energy Technology Data Exchange (ETDEWEB)
Pon, R K; Cardenas, A F; Buttler, D J
2007-09-19
The definition of what makes an article interesting varies from user to user and continually evolves even for a single user. As a result, for news recommendation systems, useless document features can not be determined a priori and all features are usually considered for interestingness classification. Consequently, the presence of currently useless features degrades classification performance [1], particularly over the initial set of news articles being classified. The initial set of document is critical for a user when considering which particular news recommendation system to adopt. To address these problems, we introduce an improved version of the naive Bayes classifier with online feature selection. We use correlation to determine the utility of each feature and take advantage of the conditional independence assumption used by naive Bayes for online feature selection and classification. The augmented naive Bayes classifier performs 28% better than the traditional naive Bayes classifier in recommending news articles from the Yahoo! RSS feeds.
Directory of Open Access Journals (Sweden)
Stephen W Hartley
2012-09-01
Full Text Available Genome-wide association studies (GWAS have identified numerous associations between genetic loci and individual phenotypes; however, relatively few GWAS have attempted to detect pleiotropic associations, in which loci are simultaneously associated with multiple distinct phenotypes. We show that pleiotropic associations can be directly modeled via the construction of simple Bayesian networks, and that these models can be applied to produce single or ensembles of Bayesian classifiers that leverage pleiotropy to improve genetic risk prediction.The proposed method includes two phases: (1 Bayesian model comparison, to identify SNPs associated with one or more traits; and (2 cross validation feature selection, in which a final set of SNPs is selected to optimize prediction.To demonstrate the capabilities and limitations of the method, a total of 1600 case-control GWAS datasets with 2 dichotomous phenotypes were simulated under 16 scenarios, varying the association strengths of causal SNPs, the size of the discovery sets, the balance between cases and controls, and the number of pleiotropic causal SNPs.Across the 16 scenarios, prediction accuracy varied from 90% to 50%. In the 14 scenarios that included pleiotropically-associated SNPs, the pleiotropic model search and prediction methods consistently outperformed the naive model search and prediction. In the 2 scenarios in which there were no true pleiotropic SNPs, the differences between the pleiotropic and naive model searches were minimal.
Pumpe, Daniel; Müller, Ewald; Enßlin, Torsten A
2016-01-01
Stochastic differential equations describe well many physical, biological and sociological systems, despite the simplification often made in their derivation. Here the usage of simple stochastic differential equations to characterize and classify complex dynamical systems is proposed within a Bayesian framework. To this end, we develop a dynamic system classifier (DSC). The DSC first abstracts training data of a system in terms of time dependent coefficients of the descriptive stochastic differential equation. Thereby the DSC identifies unique correlation structures within the training data. For definiteness we restrict the presentation of DSC to oscillation processes with a time dependent frequency {\\omega}(t) and damping factor {\\gamma}(t). Although real systems might be more complex, this simple oscillator captures many characteristic features. The {\\omega} and {\\gamma} timelines represent the abstract system characterization and permit the construction of efficient signal classifiers. Numerical experiment...
类相关性影响可变选择性贝叶斯分类器%A Selective Bayesian Classifier Based on Change of Class Relevance Influence
Institute of Scientific and Technical Information of China (English)
程玉虎; 仝瑶瑶; 王雪松
2011-01-01
A selective Bayesian classifier based on change of class relevance influence (CCRI SBC) was proposed by introducing a regulator factor into an attribute selection method, namely maximum relevance and minimum redundancy (mRMR). The regulator factor was used to change the influence degree of class relevance on the attribute selection, which can avoid the existence of redundant attributes in mRMR. In addition, a Bayesian information criterion was used to determine the optimal number of attributes automatically, which can overcome the randomness of classification results that easily caused by the setting number of attributes manually.In order to further make the CCRI SBC is applicable for continuous data,a discretization method,I.e. .equal frequency class attribute interdependent maximization was proposed, which has advantages of high classification correct rate and short discretization time.Experimental results on UC1 datasets show that the proposed method can deal with the classification problem for discrete or continuous and high-dimensional data effectively.%在最大相关最小冗余（mRMR）属性选择方法的基础上,通过设置一个调节因子来改变类别相关性在属性选择中的影响程度,解决mRMR方法易于引入冗余属性的问题,提出一种类相关性影响可变选择性贝叶斯分类器(CCRI SBC).为克服人为指定属性个数易于导致的分类结果随意性,采用贝叶斯信息准则来自动确定最优属性个数.为使CCRI SBC能够处理含有连续变量的数据集,提出等频类别依赖最大化离散化方法,具有分类准确率高和离散化时间短的优点.UCI数据集的实验结果表明,本文方法能够有效处理离散和连续高维数据的分类问题.
Dynamic Dimensionality Selection for Bayesian Classifier Ensembles
2015-03-19
bias of A1DE with minimal computational overhead. We here generalize that strategy to MI-weighted AnDE, using ws = MI(S, Y ), MI(s, Y ) = ∑ y∈Y ∑ xs...Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any...efficient in learning the model. WNANJE can model higher-order attribute interdependencies. 15. SUBJECT TERMS Big data, Low bias
Institute of Scientific and Technical Information of China (English)
邱桂华
2012-01-01
Covert channel is a transfer scheme which can leak confidential information in information system covertly. Network covert channel is crucial to network data security protection and even to cloud computing platform' s, and becomes the focus of the information security field. In this paper, we present an example of network time covert channel which is based on the time intervals of SSH packets. The SSH protocol-based interval characteristics are analysed and a new detection approach based on Bayesian classifier is designed. The experiment results show that the covert channels can be detected at the accurate rate of 95% using this approach, which shows that the detection performance is excellent.%隐蔽信道是一种能够以难以察觉的方式泄漏信息系统机密信息的通信方式.网络隐蔽信道是信息安全领域的研究热点,对保护网络数据安全以及于云计算平台数据安全至关重要.提出一种基于SSH数据包间隔时间的网络时间隐蔽信道实例；基于SSH协议的时间间隔特征,设计一种基于Bayesian分类器的检测方法；实验结果证明该检测方法能够达到95％的准确率,具有很好的检测性能.
Human Detection Using Random Color Similarity Feature and Random Ferns Classifier.
Zhang, Miaohui; Xin, Ming
2016-01-01
We explore a novel approach for human detection based on random color similarity feature (RCS) and random ferns classifier which is also known as semi-naive Bayesian classifier. In contrast to other existing features employed by human detection, color-based features are rarely used in vision-based human detection because of large intra-class variations. In this paper, we propose a novel color-based feature, RCS feature, which is yielded by simple color similarity computation between image cells randomly picked in still images, and can effectively characterize human appearances. In addition, a histogram of oriented gradient based local binary feature (HOG-LBF) is also introduced to enrich the human descriptor set. Furthermore, random ferns classifier is used in the proposed approach because of its faster speed in training and testing than traditional classifiers such as Support Vector Machine (SVM) classifier, without a loss in performance. Finally, the proposed method is conducted in public datasets and achieves competitive detection results.
DEFF Research Database (Denmark)
Sommerlund, Julie
2006-01-01
This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... and integration possible, the field of molecular biology seems to be overwhelmingly homogeneous, and in need of heterogeneity and conflict to add drive and momentum to the work being carried out. The paper is based on observations of daily life in a molecular microbiology laboratory at the Technical University...
Duzen, Carl; And Others
1992-01-01
Presents a series of activities that utilizes a leveling device to classify constant and accelerated motion. Applies this classification system to uniform circular motion and motion produced by gravitational force. (MDH)
Lee, Jae-Neung; Lee, Myung-Won; Byeon, Yeong-Hyeon; Lee, Won-Sik; Kwak, Keun-Chang
2016-01-01
In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider’s hip motion from the sensor information collected by inertial sensors as feature data for the classification of a horse’s gaits. Furthermore, we develop a coaching system under both real horse riding and simulator environments and propose a method for analyzing the rider’s motion. Using the results of the analysis, the rider can be coached in the correct motion corresponding to the classified gait. To construct a motion database, the data collected from 16 inertial sensors attached to a motion capture suit worn by one of the country’s top-level horse riding experts were used. Experiments using the original motion data and the transformed motion data were conducted to evaluate the classification performance using various classifiers. The experimental results revealed that the presented FCM-NFC showed a better accuracy performance (97.5%) than a neural network classifier (NNC), naive Bayesian classifier (NBC), and radial basis function network classifier (RBFNC) for the transformed motion data. PMID:27171098
Ranking Features on Psychological Dynamics of Cooperative Team Work through Bayesian Networks
Directory of Open Access Journals (Sweden)
Pilar Fuster-Parra
2016-05-01
Full Text Available The aim of this study is to rank some features that characterize the psychological dynamics of cooperative team work in order to determine priorities for interventions and formation: leading positive feedback, cooperative manager and collaborative manager features. From a dataset of 20 cooperative sport teams (403 soccer players, the characteristics of the prototypical sports teams are studied using an average Bayesian network (BN and two special types of BNs, the Bayesian classifiers: naive Bayes (NB and tree augmented naive Bayes (TAN. BNs are selected as they are able to produce probability estimates rather than predictions. BN results show that the antecessors (the “top” features ranked are the team members’ expectations and their attraction to the social aspects of the task. The main node is formed by the cooperative behaviors, the consequences ranked at the BN bottom (ratified by the TAN trees and the instantiations made, the roles assigned to the members and their survival inside the same team. These results should help managers to determine contents and priorities when they have to face team-building actions.
Donato, Gianluca; Bartlett, Marian Stewart; Hager, Joseph C.; Ekman, Paul; Sejnowski, Terrence J.
2010-01-01
The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions. PMID:21188284
Vargas Cardona, Hernán Darío; Orozco, Álvaro Ángel; Álvarez, Mauricio A
2013-01-01
Automatic identification of biosignals is one of the more studied fields in biomedical engineering. In this paper, we present an approach for the unsupervised recognition of biomedical signals: Microelectrode Recordings (MER) and Electrocardiography signals (ECG). The unsupervised learning is based in classic and bayesian estimation theory. We employ gaussian mixtures models with two estimation methods. The first is derived from the frequentist estimation theory, known as Expectation-Maximization (EM) algorithm. The second is obtained from bayesian probabilistic estimation and it is called variational inference. In this framework, both methods are used for parameters estimation of Gaussian mixtures. The mixtures models are used for unsupervised pattern classification, through the responsibility matrix. The algorithms are applied in two real databases acquired in Parkinson's disease surgeries and electrocardiograms. The results show an accuracy over 85% in MER and 90% in ECG for identification of two classes. These results are statistically equal or even better than parametric (Naive Bayes) and nonparametric classifiers (K-nearest neighbor).
Vitalism in naive biological thinking.
Morris, S C; Taplin, J E; Gelman, S A
2000-09-01
Vitalism is the belief that internal bodily organs have agency and that they transmit or exchange a vital force or energy. Three experiments investigated the use of vitalistic explanations for biological phenomena by 5- and 10-year-old English-speaking children and adults, focusing on 2 components: the notion that bodily organs have intentions and the notion that some life force or energy is transmitted. The original Japanese finding of vitalistic thinking was replicated in Experiment 1 with English-speaking 5-year-olds. Experiment 2 indicated that the more active component of vitalism for these children is a belief in the transfer of energy during biological processes, and Experiment 3 suggested an additional, albeit lesser, role for organ intentionality. A belief in vital energy may serve a causal placeholder function within a naive theory of biology until a more precisely formulated mechanism is known.
Supervised Classification: The Naive Beyesian Returns to the Old Bailey
Directory of Open Access Journals (Sweden)
Vilja Hulden
2014-12-01
Full Text Available A few years back, William Turkel wrote a series of blog posts called A Naive Bayesian in the Old Bailey, which showed how one could use machine learning to extract interesting documents out of a digital archive. This tutorial is a kind of an update on that blog essay, with roughly the same data but a slightly different version of the machine learner. The idea is to show why machine learning methods are of interest to historians, as well as to present a step-by-step implementation of a supervised machine learner. This learner is then applied to the Old Bailey digital archive, which contains several centuries’ worth of transcripts of trials held at the Old Bailey in London. We will be using Python for the implementation.
Lesaffre, Emmanuel
2012-01-01
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd
新家, 健精
2013-01-01
© 2012 Springer Science+Business Media, LLC. All rights reserved. Article Outline: Glossary Definition of the Subject and Introduction The Bayesian Statistical Paradigm Three Examples Comparison with the Frequentist Statistical Paradigm Future Directions Bibliography
Bernardo, Jose M
2000-01-01
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critica
Villalba, Jesús
2015-01-01
In this document we are going to derive the equations needed to implement a Variational Bayes estimation of the parameters of the simplified probabilistic linear discriminant analysis (SPLDA) model. This can be used to adapt SPLDA from one database to another with few development data or to implement the fully Bayesian recipe. Our approach is similar to Bishop's VB PPCA.
Winand, Raf; Theys, Kristof; Eusébio, Mónica; Aerts, Jan; Camacho, Ricardo J.; Gomes, Perpetua; Suchard, Marc A.; Vandamme, Anne-Mieke; Abecasis, Ana B.
2015-01-01
Objectives: Surveillance drug resistance mutations (SDRMs) in drug-naive patients are typically used to survey HIV-1-transmitted drug resistance (TDR). We test here how SDRMs in patients failing treatment, the original source of TDR, contribute to assessing TDR, transmissibility and transmission source of SDRMs. Design: This is a retrospective observational study analyzing a Portuguese cohort of HIV-1-infected patients. Methods: The prevalence of SDRMs to protease inhibitors, nucleoside reverse transcriptase inhibitors (NRTIs) and nonnucleoside reverse transcriptase inhibitors (NNRTIs) in drug-naive and treatment-failing patients was measured for 3554 HIV-1 subtype B patients. Transmission ratio (prevalence in drug-naive/prevalence in treatment-failing patients), average viral load and robust linear regression with outlier detection (prevalence in drug-naive versus in treatment-failing patients) were analyzed and used to interpret transmissibility. Results: Prevalence of SDRMs in drug-naive and treatment-failing patients were linearly correlated, but some SDRMs were classified as outliers – above (PRO: D30N, N88D/S, L90 M, RT: G190A/S/E) or below (RT: M184I/V) expectations. The normalized regression slope was 0.073 for protease inhibitors, 0.084 for NRTIs and 0.116 for NNRTIs. Differences between SDRMs transmission ratios were not associated with differences in viral loads. Conclusion: The significant linear correlation between prevalence of SDRMs in drug-naive and in treatment-failing patients indicates that the prevalence in treatment-failing patients can be useful to predict levels of TDR. The slope is a cohort-dependent estimate of rate of TDR per drug class and outlier detection reveals comparative persistence of SDRMs. Outlier SDRMs with higher transmissibility are more persistent and more likely to have been acquired from drug-naive patients. Those with lower transmissibility have faster reversion dynamics after transmission and are associated with
Neural network classification - A Bayesian interpretation
Wan, Eric A.
1990-01-01
The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical framework.
Hedlund, Jonas
2014-01-01
This paper introduces private sender information into a sender-receiver game of Bayesian persuasion with monotonic sender preferences. I derive properties of increasing differences related to the precision of signals and use these to fully characterize the set of equilibria robust to the intuitive criterion. In particular, all such equilibria are either separating, i.e., the sender's choice of signal reveals his private information to the receiver, or fully disclosing, i.e., the outcome of th...
Kirstein, Roland
2005-01-01
This paper presents a modification of the inspection game: The ?Bayesian Monitoring? model rests on the assumption that judges are interested in enforcing compliant behavior and making correct decisions. They may base their judgements on an informative but imperfect signal which can be generated costlessly. In the original inspection game, monitoring is costly and generates a perfectly informative signal. While the inspection game has only one mixed strategy equilibrium, three Perfect Bayesia...
Naive Bayes-Guided Bat Algorithm for Feature Selection
Directory of Open Access Journals (Sweden)
Ahmed Majid Taha
2013-01-01
Full Text Available When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.
Bessiere, Pierre; Ahuactzin, Juan Manuel; Mekhnacha, Kamel
2013-01-01
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean
Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces.
Onishi, Akinari; Natsume, Kiyohisa
2014-01-01
A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.
Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces.
Directory of Open Access Journals (Sweden)
Akinari Onishi
Full Text Available A P300-based brain-computer interface (BCI enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300. In this study, we evaluated ensemble linear discriminant analysis (LDA classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA, or none. The results show that an ensemble stepwise LDA (SWLDA classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.
Exploiting missing clinical data in Bayesian network modeling for predicting medical problems.
Lin, Jau-Huei; Haug, Peter J
2008-02-01
When machine learning algorithms are applied to data collected during the course of clinical care, it is generally accepted that the data has not been consistently collected. The absence of expected data elements is common and the mechanism through which a data element is missing often involves the clinical relevance of that data element in a specific patient. Therefore, the absence of data may have information value of its own. In the process of designing an application intended to support a medical problem list, we have studied whether the "missingness" of clinical data can provide useful information in building prediction models. In this study, we experimented with four methods of treating missing values in a clinical data set-two of them explicitly model the absence or "missingness" of data. Each of these data sets were used to build four different kinds of Bayesian classifiers-a naive Bayes structure, a human-composed network structure, and two networks based on structural learning algorithms. We compared the performance between groups with and without explicit models of missingness using the area under the ROC curve. The results showed that in most cases the classifiers trained using the explicit missing value treatments performed better. The result suggests that information may exist in "missingness" itself. Thus, when designing a decision support system, we suggest one consider explicitly representing the presence/absence of data in the underlying logic.
Subtrochanteric fractures in bisphosphonate-naive patients
DEFF Research Database (Denmark)
Adachi, Jonathan D; Lyles, Kenneth; Boonen, Steven
2011-01-01
Our purpose was to characterize the risks of osteoporosis-related subtrochanteric fractures in bisphosphonate-naive individuals. Baseline characteristics of patients enrolled in the HORIZON-Recurrent Fracture Trial with a study-qualifying hip fracture were examined, comparing those who sustained...... incident subtrochanteric fractures with those sustaining other hip fractures. Subjects were bisphosphonate-naive or had a bisphosphonate washout period of 6-24 months and subsequently received an annual infusion of zoledronic acid 5 mg or placebo after low-trauma hip-fracture repair. In total, 2,127 men...... and women were included. Of the qualifying hip fractures, 5.2% were subtrochanteric, 54.8% femoral neck, 33.0% intertrochanteric, and 7.1% other (generally complex fractures of mixed type). Significant baseline (pre-hip fracture) differences were seen between index hip-fracture types, with the percentage...
PERBANDINGAN K-NEAREST NEIGHBOR DAN NAIVE BAYES UNTUK KLASIFIKASI TANAH LAYAK TANAM POHON JATI
Directory of Open Access Journals (Sweden)
Didik Srianto
2016-10-01
Full Text Available Data mining adalah proses menganalisa data dari perspektif yang berbeda dan menyimpulkannya menjadi informasi-informasi penting yang dapat dipakai untuk meningkatkan keuntungan, memperkecil biaya pengeluaran, atau bahkan keduanya. Secara teknis, data mining dapat disebut sebagai proses untuk menemukan korelasi atau pola dari ratusan atau ribuan field dari sebuah relasional database yang besar. Pada perum perhutani KPH SEMARANG saat ini masih menggunakan cara manual untuk menentukan jenis tanaman (jati / non jati. K-Nearest Neighbour atau k-NN merupakan algoritma data mining yang dapat digunakan untuk proses klasifikasi dan regresi. Naive bayes Classifier merupakan suatu teknik yang dapat digunakan untuk teknik klasifikasi. Pada penelitian ini k-NN dan Naive Bayes akan digunakan untuk mengklasifikasi data pohon jati dari perum perhutani KPH SEMARANG. Yang mana hasil klasifikasi dari k-NN dan Naive Bayes akan dibandingkan hasilnya. Pengujian dilakukan menggunakan software RapidMiner. Setelah dilakukan pengujian k-NN dianggap lebih baik dari Naife Bayes dengan akurasi 96.66% dan 82.63. Kata kunci -k-NN,Klasifikasi,Naive Bayes,Penanaman Pohon Jati
Yugoslav Naive Art and Popular Culture
Directory of Open Access Journals (Sweden)
Meta Kordiš
2009-12-01
After the Second World War, the Yugoslav socialist state also strove to equalize and democratize society through art by minimizing the differences between the producers and consumers of art. Such a policy led to the decentralization of culture by forming various cultural and artistic institutions and by holding cultural events and spectacles in the countryside and peripheral areas. Through these various informal ideological mechanisms, the state apparatus exercised its authority in socializing its people in the spirit of Yugoslav socialist self-management and the ideology of brotherhood and unity by joining together the producers and consumers of naive art from various ethnicities, cultures, and social classes. Unfortunately this transformed naive art at its peak of popularity into a decorative and souvenir artifact with a pastoral image and folklore motifs. The encouragement from the authorities on the one hand and the market on the other produced and reproduced simple art forms and narrative contents without a complex iconography, which were consumed uncritically and on a large scale. Consequently, this completely denied the core of naive art and resulted in its final devaluation.
Recognition of pornographic web pages by classifying texts and images.
Hu, Weiming; Wu, Ou; Chen, Zhouyao; Fu, Zhouyu; Maybank, Steve
2007-06-01
With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.
Application of a Hidden Bayes Naive Multiclass Classifier in Network Intrusion Detection
Koc, Levent
2013-01-01
With increasing Internet connectivity and traffic volume, recent intrusion incidents have reemphasized the importance of network intrusion detection systems for combating increasingly sophisticated network attacks. Techniques such as pattern recognition and the data mining of network events are often used by intrusion detection systems to classify…
Optimized Radial Basis Function Classifier for Multi Modal Biometrics
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Anand Viswanathan
2014-07-01
Full Text Available Biometric systems can be used for the identification or verification of humans based on their physiological or behavioral features. In these systems the biometric characteristics such as fingerprints, palm-print, iris or speech can be recorded and are compared with the samples for the identification or verification. Multimodal biometrics is more accurate and solves spoof attacks than the single modal bio metrics systems. In this study, a multimodal biometric system using fingerprint images and finger-vein patterns is proposed and also an optimized Radial Basis Function (RBF kernel classifier is proposed to identify the authorized users. The extracted features from these modalities are selected by PCA and kernel PCA and combined to classify by RBF classifier. The parameters of RBF classifier is optimized by using BAT algorithm with local search. The performance of the proposed classifier is compared with the KNN classifier, Naïve Bayesian classifier and non-optimized RBF classifier.
Introduction to Bayesian statistics
Bolstad, William M
2017-01-01
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...
Bayesian artificial intelligence
Korb, Kevin B
2003-01-01
As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.
DEFF Research Database (Denmark)
Jensen, Finn Verner; Nielsen, Thomas Dyhre
2016-01-01
Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes...... is largely due to the availability of efficient inference algorithms for answering probabilistic queries about the states of the variables in the network. Furthermore, to support the construction of Bayesian network models, learning algorithms are also available. We give an overview of the Bayesian network...
Do the Naive Know Best? The Predictive Power of Naive Ratings of Couple Interactions
Baucom, Katherine J. W.; Baucom, Brian R.; Christensen, Andrew
2012-01-01
We examined the utility of naive ratings of communication patterns and relationship quality in a large sample of distressed couples. Untrained raters assessed 10-min videotaped interactions from 134 distressed couples who participated in both problem-solving and social support discussions at each of 3 time points (pre-therapy, post-therapy, and…
Search for naive human pluripotent stem cells
Institute of Scientific and Technical Information of China (English)
Simone Aparecida Siqueira Fonseca; Roberta Montero Costas; Lygia Veiga Pereira
2015-01-01
Normal mouse pluripotent stem cells were originallyderived from the inner cell mass （ICM） of blastocystsand shown to be the in vitro equivalent of those preimplantationembryonic cells, and thus were calledembryonic stem cells （ESCs）. More than a decade later,pluripotent cells were isolated from the ICM of humanblastocysts. Despite being called human ESCs, thesecells differ significantly from mouse ESCs, includingdifferent morphology and mechanisms of control ofpluripotency, suggesting distinct embryonic originsof ESCs from the two species. Subsequently, mousepluripotent stem cells were established from the ICMderivedepiblast of post-implantation embryos. Thesemouse epiblast stem cells （EpiSCs） are morphologicaland epigenetically more similar to human ESCs. Thisraised the question of whether cells from the humanICM are in a more advanced differentiation stage thantheir murine counterpart, or whether the availableculture conditions were not adequate to maintain thosehuman cells in their in vivo state, leading to a transitioninto EpiSC-like cells in vitro . More recently, novel cultureconditions allowed the conversion of human ESCs intomouse ESC-like cells called naive （or ground state）human ESCs, and the derivation of naive human ESCsfrom blastocysts. Here we will review the characteristicsof each type of pluripotent stem cells, how （andwhether） these relate to different stages of embryonicdevelopment, and discuss the potential implications ofnaive human ESCs in research and therapy.
Brut: Automatic bubble classifier
Beaumont, Christopher; Goodman, Alyssa; Williams, Jonathan; Kendrew, Sarah; Simpson, Robert
2014-07-01
Brut, written in Python, identifies bubbles in infrared images of the Galactic midplane; it uses a database of known bubbles from the Milky Way Project and Spitzer images to build an automatic bubble classifier. The classifier is based on the Random Forest algorithm, and uses the WiseRF implementation of this algorithm.
Applied Bayesian Hierarchical Methods
Congdon, Peter D
2010-01-01
Bayesian methods facilitate the analysis of complex models and data structures. Emphasizing data applications, alternative modeling specifications, and computer implementation, this book provides a practical overview of methods for Bayesian analysis of hierarchical models.
Gelman, Andrew; Stern, Hal S; Dunson, David B; Vehtari, Aki; Rubin, Donald B
2013-01-01
FUNDAMENTALS OF BAYESIAN INFERENCEProbability and InferenceSingle-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian ApproachesHierarchical ModelsFUNDAMENTALS OF BAYESIAN DATA ANALYSISModel Checking Evaluating, Comparing, and Expanding ModelsModeling Accounting for Data Collection Decision AnalysisADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional ApproximationsREGRESSION MODELS Introduction to Regression Models Hierarchical Linear
Vitalistic causality in young children's naive biology.
Inagaki, Kayoko; Hatano, Giyoo
2004-08-01
One of the key issues in conceptual development research concerns what kinds of causal devices young children use to understand the biological world. We review evidence that children predict and interpret biological phenomena, especially human bodily processes, on the basis of 'vitalistic causality'. That is, they assume that vital power or life force taken from food and water makes humans active, prevents them from being taken ill, and enables them to grow. These relationships are also extended readily to other animals and even to plants. Recent experimental results show that a majority of preschoolers tend to choose vitalistic explanations as most plausible. Vitalism, together with other forms of intermediate causality, constitute unique causal devices for naive biology as a core domain of thought.
Bayes classifiers for imbalanced traffic accidents datasets.
Mujalli, Randa Oqab; López, Griselda; Garach, Laura
2016-03-01
Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.
改进的朴素贝叶斯聚类Web文本分类挖掘技术%The Improved Naive Bayes Text Classification Data Mining Clustering Web
Institute of Scientific and Technical Information of China (English)
高胜利
2012-01-01
通过对Web数据的特点进行详细的分析,在基于传统的贝叶斯聚类算法基础上,采用网页标记形式来有效地弥补朴素贝叶斯算法的不足,并将改进的方法应用在文本分类中,是一种很好的改进思路。最后实验结果也表明,此方法能够有效地对文本进行分类。%This paper first introduced the Web mining and text classification of basic theory, specific to the Web data characteristics are analyzed in detail, mainly based on the traditional Bayesian clustering algorithm based on the proposed algorithm, the improvement of the webpage, marked form to effectively compensates for the naive Bayes algorithm is in- sufficient, will be improved method and its application in text classification, finally the experimental results show that the method can effectively classify the text.
Molecular Criteria for Defining the Naive Human Pluripotent State.
Theunissen, Thorold W; Friedli, Marc; He, Yupeng; Planet, Evarist; O'Neil, Ryan C; Markoulaki, Styliani; Pontis, Julien; Wang, Haoyi; Iouranova, Alexandra; Imbeault, Michaël; Duc, Julien; Cohen, Malkiel A; Wert, Katherine J; Castanon, Rosa; Zhang, Zhuzhu; Huang, Yanmei; Nery, Joseph R; Drotar, Jesse; Lungjangwa, Tenzin; Trono, Didier; Ecker, Joseph R; Jaenisch, Rudolf
2016-10-06
Recent studies have aimed to convert cultured human pluripotent cells to a naive state, but it remains unclear to what extent the resulting cells recapitulate in vivo naive pluripotency. Here we propose a set of molecular criteria for evaluating the naive human pluripotent state by comparing it to the human embryo. We show that transcription of transposable elements provides a sensitive measure of the concordance between pluripotent stem cells and early human development. We also show that induction of the naive state is accompanied by genome-wide DNA hypomethylation, which is reversible except at imprinted genes, and that the X chromosome status resembles that of the human preimplantation embryo. However, we did not see efficient incorporation of naive human cells into mouse embryos. Overall, the different naive conditions we tested showed varied relationships to human embryonic states based on molecular criteria, providing a backdrop for future analysis of naive human pluripotency. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Yeom, Ha-Neul
2014-09-01
Full Text Available In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.
Classifying Returns as Extreme
DEFF Research Database (Denmark)
Christiansen, Charlotte
2014-01-01
I consider extreme returns for the stock and bond markets of 14 EU countries using two classification schemes: One, the univariate classification scheme from the previous literature that classifies extreme returns for each market separately, and two, a novel multivariate classification scheme tha...
Bayesian Games with Intentions
Directory of Open Access Journals (Sweden)
Adam Bjorndahl
2016-06-01
Full Text Available We show that standard Bayesian games cannot represent the full spectrum of belief-dependent preferences. However, by introducing a fundamental distinction between intended and actual strategies, we remove this limitation. We define Bayesian games with intentions, generalizing both Bayesian games and psychological games, and prove that Nash equilibria in psychological games correspond to a special class of equilibria as defined in our setting.
Vo, Martin
2017-08-01
Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio). Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.
Semisupervised learning using Bayesian interpretation: application to LS-SVM.
Adankon, Mathias M; Cheriet, Mohamed; Biem, Alain
2011-04-01
Bayesian reasoning provides an ideal basis for representing and manipulating uncertain knowledge, with the result that many interesting algorithms in machine learning are based on Bayesian inference. In this paper, we use the Bayesian approach with one and two levels of inference to model the semisupervised learning problem and give its application to the successful kernel classifier support vector machine (SVM) and its variant least-squares SVM (LS-SVM). Taking advantage of Bayesian interpretation of LS-SVM, we develop a semisupervised learning algorithm for Bayesian LS-SVM using our approach based on two levels of inference. Experimental results on both artificial and real pattern recognition problems show the utility of our method.
Understanding Computational Bayesian Statistics
Bolstad, William M
2011-01-01
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistic
Bayesian statistics an introduction
Lee, Peter M
2012-01-01
Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel
Bayesian of inductive cognition algorithm for adaptive classification
Jin, Longcun; Wan, Wanggen; Cui, Bin; Wu, Yongliang
2009-07-01
In this paper, we proposed a Bayesian of inductive cognition algorithm using in virtual reality multimedia classification. We present a Bayesian of inductive cognition algorithm framework model for adaptively classifying scenes in virtual reality multimedia data. The Multimedia can switch between different shots, the unknown objects can leave or enter the scene at multiple times, and the scenes can be adaptively classified. The proposed algorithm consists of Bayesian inductive cognition part and Dirichlet process part. This algorithm has several advantages over traditional distance-based agglomerative adaptively classifying algorithms. Bayesian of inductive cognition algorithm based on Dirichlet process hypothesis testing is used to decide which merges are advantageous and to output the recommended depth of the scenes. The algorithm can be interpreted as a novel fast bottom-up approximate inference method for a Dirichlet process mixture model. We describe procedures for learning the model hyperparameters, computing the predictive distribution, and extensions to the Bayesian of inductive cognition algorithm. Experimental results on virtual reality multimedia data sets demonstrate useful properties of the Bayesian of inductive cognition algorithm.
Classifier in Age classification
Directory of Open Access Journals (Sweden)
B. Santhi
2012-12-01
Full Text Available Face is the important feature of the human beings. We can derive various properties of a human by analyzing the face. The objective of the study is to design a classifier for age using facial images. Age classification is essential in many applications like crime detection, employment and face detection. The proposed algorithm contains four phases: preprocessing, feature extraction, feature selection and classification. The classification employs two class labels namely child and Old. This study addresses the limitations in the existing classifiers, as it uses the Grey Level Co-occurrence Matrix (GLCM for feature extraction and Support Vector Machine (SVM for classification. This improves the accuracy of the classification as it outperforms the existing methods.
Adaptation in P300 braincomputer interfaces: A two-classifier cotraining approach
DEFF Research Database (Denmark)
Panicker, Rajesh C.; Sun, Ying; Puthusserypady, Sadasivan
2010-01-01
A cotraining-based approach is introduced for constructing high-performance classifiers for P300-based braincomputer interfaces (BCIs), which were trained from very little data. It uses two classifiers: Fishers linear discriminant analysis and Bayesian linear discriminant analysis progressively...
Classifying Linear Canonical Relations
Lorand, Jonathan
2015-01-01
In this Master's thesis, we consider the problem of classifying, up to conjugation by linear symplectomorphisms, linear canonical relations (lagrangian correspondences) from a finite-dimensional symplectic vector space to itself. We give an elementary introduction to the theory of linear canonical relations and present partial results toward the classification problem. This exposition should be accessible to undergraduate students with a basic familiarity with linear algebra.
Intelligent Garbage Classifier
Directory of Open Access Journals (Sweden)
Ignacio Rodríguez Novelle
2008-12-01
Full Text Available IGC (Intelligent Garbage Classifier is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste.
Generalized classifier neural network.
Ozyildirim, Buse Melis; Avci, Mutlu
2013-03-01
In this work a new radial basis function based classification neural network named as generalized classifier neural network, is proposed. The proposed generalized classifier neural network has five layers, unlike other radial basis function based neural networks such as generalized regression neural network and probabilistic neural network. They are input, pattern, summation, normalization and output layers. In addition to topological difference, the proposed neural network has gradient descent based optimization of smoothing parameter approach and diverge effect term added calculation improvements. Diverge effect term is an improvement on summation layer calculation to supply additional separation ability and flexibility. Performance of generalized classifier neural network is compared with that of the probabilistic neural network, multilayer perceptron algorithm and radial basis function neural network on 9 different data sets and with that of generalized regression neural network on 3 different data sets include only two classes in MATLAB environment. Better classification performance up to %89 is observed. Improved classification performances proved the effectivity of the proposed neural network.
Discriminative Bayesian Dictionary Learning for Classification.
Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal
2016-12-01
We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.
Yuan, Ying; MacKinnon, David P.
2009-01-01
In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…
Classifying smoking urges via machine learning.
Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin
2016-12-01
Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights
von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo
2014-06-01
Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.
Directory of Open Access Journals (Sweden)
R. Harald Baayen
2011-01-01
Full Text Available Three classifiers from machine learning (the generalized linear mixed model, memory based learning, and support vector machines are compared with a naive discriminative learning classifier, derived from basic principles of error-driven learning characterizing animal and human learning. Tested on the dative alternation in English, using the Switchboard data from (BRESNAN; CUENI; NIKITINA; BAAYEN, 2007, naive discriminative learning emerges with stateof-the-art predictive accuracy. Naive discriminative learning offers a united framework for understanding the learning of probabilistic distributional patterns, for classification, and for a cognitive grounding of distinctive collexeme analysis.Três classificadores de aprendizagem de máquina (modelos mistos lineares generalizados, aprendizagem baseada na memória e máquinas de apoio a vetores são comparados com o classificador da aprendizagem discriminativa ingênua, derivada de princípios básicos da aprendizagem guiada por erros de humanos e animais. Testada na alternância dativa do inglês, usando os dados do Switchboard (BRESNAN; CUENI; NIKITINA; BAAYEN, 2007, a aprendizagem discriminativa ingênua emerge com uma acurácia predicativa no estado da arte. A aprendizagem discriminativa ingênua oferece um arcabouço unificado para a compreensão da aprendizagem de padrões distribucionais probabilísticos, para a classificação, e para um embasamento cognitivo para a análise de colexemas distintivos.
Classification using Hierarchical Naive Bayes models
DEFF Research Database (Denmark)
Langseth, Helge; Dyhre Nielsen, Thomas
2006-01-01
Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe...... an instance are conditionally independent given the class of that instance. When this assumption is violated (which is often the case in practice) it can reduce classification accuracy due to “information double-counting” and interaction omission. In this paper we focus on a relatively new set of models......, termed Hierarchical Naïve Bayes models. Hierarchical Naïve Bayes models extend the modeling flexibility of Naïve Bayes models by introducing latent variables to relax some of the independence statements in these models. We propose a simple algorithm for learning Hierarchical Naïve Bayes models...
Konstruksi Bayesian Network Dengan Algoritma Bayesian Association Rule Mining Network
Octavian
2015-01-01
Beberapa tahun terakhir, Bayesian Network telah menjadi konsep yang populer digunakan dalam berbagai bidang kehidupan seperti dalam pengambilan sebuah keputusan dan menentukan peluang suatu kejadian dapat terjadi. Sayangnya, pengkonstruksian struktur dari Bayesian Network itu sendiri bukanlah hal yang sederhana. Oleh sebab itu, penelitian ini mencoba memperkenalkan algoritma Bayesian Association Rule Mining Network untuk memudahkan kita dalam mengkonstruksi Bayesian Network berdasarkan data ...
Ramirez, Lorenzo A; Daniel, Alexander; Frank, Ian; Tebas, Pablo; Boyer, Jean D
2014-08-15
Human immunodeficiency virus type 1 (HIV-1)-infected individuals, despite receipt of antiretroviral therapy (ART), often have impaired vaccine responses. We examined the role that immune activation and cellular phenotypes play in influenza A(H1N1) vaccine responsiveness in HIV-infected subjects receiving ART. Subjects received the H1N1 vaccine (15-µg dose; Novartis), and antibody titers at baseline and after immunization were evaluated. Subjects were classified as responders if, by week 3, seroprotection guidelines were met. Responders had higher percentages of baseline naive T cells and lower percentages of terminally differentiated T cells, compared with nonresponders. Additionally, the naive CD4(+) T-cell percentage and age were negatively correlated. Preservation of naive T-cell populations by starting therapy early could impact vaccine responses against influenza virus and other pathogens, especially as this population ages.
Risk of Erectile Dysfunction in Transfusion-naive Thalassemia Men
Chen, Yu-Guang; Lin, Te-Yu; Lin, Cheng-Li; Dai, Ming-Shen; Ho, Ching-Liang; Kao, Chia-Hung
2015-01-01
Abstract Based on the mechanism of pathophysiology, thalassemia major or transfusion-dependent thalassemia patients may have an increased risk of developing organic erectile dysfunction resulting from hypogonadism. However, there have been few studies investigating the association between erectile dysfunction and transfusion-naive thalassemia populations. We constructed a population-based cohort study to elucidate the association between transfusion-naive thalassemia populations and organic erectile dysfunction This nationwide population-based cohort study involved analyzing data from 1998 to 2010 obtained from the Taiwanese National Health Insurance Research Database, with a follow-up period extending to the end of 2011. We identified men with transfusion-naive thalassemia and selected a comparison cohort that was frequency-matched with these according to age, and year of diagnosis thalassemia at a ratio of 1 thalassemia man to 4 control men. We analyzed the risks for transfusion-naive thalassemia men and organic erectile dysfunction by using Cox proportional hazards regression models. In this study, 588 transfusion-naive thalassemia men and 2337 controls were included. Total 12 patients were identified within the thalassaemia group and 10 within the control group. The overall risks for developing organic erectile dysfunction were 4.56-fold in patients with transfusion-naive thalassemia men compared with the comparison cohort after we adjusted for age and comorbidities. Our long-term cohort study results showed that in transfusion-naive thalassemia men, there was a higher risk for the development of organic erectile dysfunction, particularly in those patients with comorbidities. PMID:25837766
Directory of Open Access Journals (Sweden)
Krzysztof Tomanek
2014-05-01
Full Text Available The purpose of this article is to present the basic methods for classifying text data. These methods make use of achievements earned in areas such as: natural language processing, the analysis of unstructured data. I introduce and compare two analytical techniques applied to text data. The first analysis makes use of thematic vocabulary tool (sentiment analysis. The second technique uses the idea of Bayesian classification and applies, so-called, naive Bayes algorithm. My comparison goes towards grading the efficiency of use of these two analytical techniques. I emphasize solutions that are to be used to build dictionary accurate for the task of text classification. Then, I compare supervised classification to automated unsupervised analysis’ effectiveness. These results reinforce the conclusion that a dictionary which has received good evaluation as a tool for classification should be subjected to review and modification procedures if is to be applied to new empirical material. Adaptation procedures used for analytical dictionary become, in my proposed approach, the basic step in the methodology of textual data analysis.
High Performance Medical Classifiers
Fountoukis, S. G.; Bekakos, M. P.
2009-08-01
In this paper, parallelism methodologies for the mapping of machine learning algorithms derived rules on both software and hardware are investigated. Feeding the input of these algorithms with patient diseases data, medical diagnostic decision trees and their corresponding rules are outputted. These rules can be mapped on multithreaded object oriented programs and hardware chips. The programs can simulate the working of the chips and can exhibit the inherent parallelism of the chips design. The circuit of a chip can consist of many blocks, which are operating concurrently for various parts of the whole circuit. Threads and inter-thread communication can be used to simulate the blocks of the chips and the combination of block output signals. The chips and the corresponding parallel programs constitute medical classifiers, which can classify new patient instances. Measures taken from the patients can be fed both into chips and parallel programs and can be recognized according to the classification rules incorporated in the chips and the programs design. The chips and the programs constitute medical decision support systems and can be incorporated into portable micro devices, assisting physicians in their everyday diagnostic practice.
Adaptive statistical pattern classifiers for remotely sensed data
Gonzalez, R. C.; Pace, M. O.; Raulston, H. S.
1975-01-01
A technique for the adaptive estimation of nonstationary statistics necessary for Bayesian classification is developed. The basic approach to the adaptive estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest and (2) a projection of the parameters in time or position. A divergence criterion is developed to monitor algorithm performance. Comparative results of adaptive and nonadaptive classifier tests are presented for simulated four dimensional spectral scan data.
Model Diagnostics for Bayesian Networks
Sinharay, Sandip
2006-01-01
Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…
Classifying depth of anesthesia using EEG features, a comparison.
Esmaeili, Vahid; Shamsollahi, Mohammad Bagher; Arefian, Noor Mohammad; Assareh, Amin
2007-01-01
Various EEG features have been used in depth of anesthesia (DOA) studies. The objective of this study was to find the excellent features or combination of them than can discriminate between different anesthesia states. Conducting a clinical study on 22 patients we could define 4 distinct anesthetic states: awake, moderate, general anesthesia, and isoelectric. We examined features that have been used in earlier studies using single-channel EEG signal processing method. The maximum accuracy (99.02%) achieved using approximate entropy as the feature. Some other features could well discriminate a particular state of anesthesia. We could completely classify the patterns by means of 3 features and Bayesian classifier.
Derivation of novel human ground state naive pluripotent stem cells.
Gafni, Ohad; Weinberger, Leehee; Mansour, Abed AlFatah; Manor, Yair S; Chomsky, Elad; Ben-Yosef, Dalit; Kalma, Yael; Viukov, Sergey; Maza, Itay; Zviran, Asaf; Rais, Yoach; Shipony, Zohar; Mukamel, Zohar; Krupalnik, Vladislav; Zerbib, Mirie; Geula, Shay; Caspi, Inbal; Schneir, Dan; Shwartz, Tamar; Gilad, Shlomit; Amann-Zalcenstein, Daniela; Benjamin, Sima; Amit, Ido; Tanay, Amos; Massarwa, Rada; Novershtern, Noa; Hanna, Jacob H
2013-12-12
Mouse embryonic stem (ES) cells are isolated from the inner cell mass of blastocysts, and can be preserved in vitro in a naive inner-cell-mass-like configuration by providing exogenous stimulation with leukaemia inhibitory factor (LIF) and small molecule inhibition of ERK1/ERK2 and GSK3β signalling (termed 2i/LIF conditions). Hallmarks of naive pluripotency include driving Oct4 (also known as Pou5f1) transcription by its distal enhancer, retaining a pre-inactivation X chromosome state, and global reduction in DNA methylation and in H3K27me3 repressive chromatin mark deposition on developmental regulatory gene promoters. Upon withdrawal of 2i/LIF, naive mouse ES cells can drift towards a primed pluripotent state resembling that of the post-implantation epiblast. Although human ES cells share several molecular features with naive mouse ES cells, they also share a variety of epigenetic properties with primed murine epiblast stem cells (EpiSCs). These include predominant use of the proximal enhancer element to maintain OCT4 expression, pronounced tendency for X chromosome inactivation in most female human ES cells, increase in DNA methylation and prominent deposition of H3K27me3 and bivalent domain acquisition on lineage regulatory genes. The feasibility of establishing human ground state naive pluripotency in vitro with equivalent molecular and functional features to those characterized in mouse ES cells remains to be defined. Here we establish defined conditions that facilitate the derivation of genetically unmodified human naive pluripotent stem cells from already established primed human ES cells, from somatic cells through induced pluripotent stem (iPS) cell reprogramming or directly from blastocysts. The novel naive pluripotent cells validated herein retain molecular characteristics and functional properties that are highly similar to mouse naive ES cells, and distinct from conventional primed human pluripotent cells. This includes competence in the generation
Classifiers and Plurality: evidence from a deictic classifier language
Directory of Open Access Journals (Sweden)
Filomena Sandalo
2016-12-01
Full Text Available This paper investigates the semantic contribution of plural morphology and its interaction with classifiers in Kadiwéu. We show that Kadiwéu, a Waikurúan language spoken in South America, is a classifier language similar to Chinese but classifiers are an obligatory ingredient of all determiner-like elements, such as quantifiers, numerals, and wh-words for arguments. What all elements with classifiers have in common is that they contribute an atomized/individualized interpretation of the NP. Furthermore, this paper revisits the relationship between classifiers and number marking and challenges the common assumption that classifiers and plurals are mutually exclusive.
Bayesian data analysis for newcomers.
Kruschke, John K; Liddell, Torrin M
2017-04-12
This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.
Energy Technology Data Exchange (ETDEWEB)
Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory
2009-01-01
Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.
Classifying TDSS Stellar Variables
Amaro, Rachael Christina; Green, Paul J.; TDSS Collaboration
2017-01-01
The Time Domain Spectroscopic Survey (TDSS), a subprogram of SDSS-IV eBOSS, obtains classification/discovery spectra of point-source photometric variables selected from PanSTARRS and SDSS multi-color light curves regardless of object color or lightcurve shape. Tens of thousands of TDSS spectra are already available and have been spectroscopically classified both via pipeline and by visual inspection. About half of these spectra are quasars, half are stars. Our goal is to classify the stars with their correct variability types. We do this by acquiring public multi-epoch light curves for brighter stars (rSky Survey (CSS). We then run a number of light curve analyses from VARTOOLS, a program for analyzing astronomical time-series data, to constrain variable type both for broad statistics relevant to future surveys like the Transiting Exoplanet Survey Satellite (TESS) and the Large Synoptic Survey Telescope (LSST), and to find the inevitable exotic oddballs that warrant further follow-up. Specifically, the Lomb-Scargle Periodogram and the Box-Least Squares Method are being implemented and tested against their known variable classifications and parameters in the Catalina Surveys Periodic Variable Star Catalog. Variable star classifications include RR Lyr, close eclipsing binaries, CVs, pulsating white dwarfs, and other exotic systems. The key difference between our catalog and others is that along with the light curves, we will be using TDSS spectra to help in the classification of variable type, as spectra are rich with information allowing estimation of physical parameters like temperature, metallicity, gravity, etc. This work was supported by the SDSS Research Experience for Undergraduates program, which is funded by a grant from Sloan Foundation to the Astrophysical Research Consortium.
MScanner: a classifier for retrieving Medline citations
Directory of Open Access Journals (Sweden)
Altman Russ B
2008-02-01
Full Text Available Abstract Background Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline. However, ad-hoc retrieval systems do not meet all of the needs of databases that curate information from literature, or of text miners developing a corpus on a topic that has many terms indicative of relevance. Several databases have developed supervised learning methods that operate on a filtered subset of Medline, to classify Medline records so that fewer articles have to be manually reviewed for relevance. A few studies have considered generalisation of Medline classification to operate on the entire Medline database in a non-domain-specific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains. Results MScanner is an implementation of a Bayesian classifier that provides a simple web interface for submitting a corpus of relevant training examples in the form of PubMed IDs and returning results ranked by decreasing probability of relevance. For maximum speed it uses the Medical Subject Headings (MeSH and journal of publication as a concise document representation, and takes roughly 90 seconds to return results against the 16 million records in Medline. The web interface provides interactive exploration of the results, and cross validated performance evaluation on the relevant input against a random subset of Medline. We describe the classifier implementation, cross validate it on three domain-specific topics, and compare its performance to that of an expert PubMed query for a complex topic. In cross validation on the three sample topics against 100,000 random articles, the classifier achieved excellent separation of relevant and irrelevant article score distributions, ROC areas between 0.97 and 0.99, and averaged precision between 0.69 and 0.92. Conclusion MScanner is an effective non-domain-specific classifier that operates on the entire Medline
Thermal bioaerosol cloud tracking with Bayesian classification
Smith, Christian W.; Dupuis, Julia R.; Schundler, Elizabeth C.; Marinelli, William J.
2017-05-01
The development of a wide area, bioaerosol early warning capability employing existing uncooled thermal imaging systems used for persistent perimeter surveillance is discussed. The capability exploits thermal imagers with other available data streams including meteorological data and employs a recursive Bayesian classifier to detect, track, and classify observed thermal objects with attributes consistent with a bioaerosol plume. Target detection is achieved based on similarity to a phenomenological model which predicts the scene-dependent thermal signature of bioaerosol plumes. Change detection in thermal sensor data is combined with local meteorological data to locate targets with the appropriate thermal characteristics. Target motion is tracked utilizing a Kalman filter and nearly constant velocity motion model for cloud state estimation. Track management is performed using a logic-based upkeep system, and data association is accomplished using a combinatorial optimization technique. Bioaerosol threat classification is determined using a recursive Bayesian classifier to quantify the threat probability of each tracked object. The classifier can accept additional inputs from visible imagers, acoustic sensors, and point biological sensors to improve classification confidence. This capability was successfully demonstrated for bioaerosol simulant releases during field testing at Dugway Proving Grounds. Standoff detection at a range of 700m was achieved for as little as 500g of anthrax simulant. Developmental test results will be reviewed for a range of simulant releases, and future development and transition plans for the bioaerosol early warning platform will be discussed.
Fox, G.J.A.; Berg, van den S.M.; Veldkamp, B.P.; Irwing, P.; Booth, T.; Hughes, D.
2015-01-01
In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item resp
Fox, Gerardus J.A.; van den Berg, Stéphanie Martine; Veldkamp, Bernard P.; Irwing, P.; Booth, T.; Hughes, D.
2015-01-01
In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item
Granade, Christopher; Cory, D G
2015-01-01
In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of- the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a lack of informative prior distributions, and an inability to track time-dependent processes. Here, we solve all three problems. First, we use modern statistical methods, as pioneered by Husz\\'ar and Houlsby and by Ferrie, to make Bayesian tomography numerically tractable. Our approach allows for practical computation of Bayesian point and region estimators for quantum states and channels. Second, we propose the first informative priors on quantum states and channels. Finally, we develop a method that allows online tracking of time-dependent states and estimates the drift and diffusion processes affecting a state. We provide source code and animated visual examples for our methods.
Detecting Threat E-mails using Bayesian Approach
Banday, M Tariq; Jan, Tariq R; Shah, Nisar A
2011-01-01
Fraud and terrorism have a close connect in terms of the processes that enables and promote them. In the era of Internet, its various services that include Web, e-mail, social networks, blogs, instant messaging, chats, etc. are used in terrorism not only for communication but also for i) creation of ideology, ii) resource gathering, iii) recruitment, indoctrination and training, iv) creation of terror network, and v) information gathering. A major challenge for law enforcement and intelligence agencies is efficient and accurate gathering of relevant and growing volume of crime data. This paper reports on use of established Na\\"ive Bayesian filter for classification of threat e-mails. Efficiency in filtering threat e-mail by use of three different Na\\"ive Bayesian filter approaches i.e. single keywords, weighted multiple keywords and weighted multiple keywords with keyword context matching are evaluated on a threat e-mail corpus created by extracting data from sources that are very close to terrorism.
Bayesian networks in neuroscience: a survey.
Bielza, Concha; Larrañaga, Pedro
2014-01-01
Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.
Pankau, Brian L.
2009-01-01
This empirical study evaluates the document category prediction effectiveness of Naive Bayes (NB) and K-Nearest Neighbor (KNN) classifier treatments built from different feature selection and machine learning settings and trained and tested against textual corpora of 2300 Gang-Of-Four (GOF) design pattern documents. Analysis of the experiment's…
Botnet analysis using ensemble classifier
Directory of Open Access Journals (Sweden)
Anchit Bijalwan
2016-09-01
Full Text Available This paper analyses the botnet traffic using Ensemble of classifier algorithm to find out bot evidence. We used ISCX dataset for training and testing purpose. We extracted the features of both training and testing datasets. After extracting the features of this dataset, we bifurcated these features into two classes, normal traffic and botnet traffic and provide labelling. Thereafter using modern data mining tool, we have applied ensemble of classifier algorithm. Our experimental results show that the performance for finding bot evidence using ensemble of classifiers is better than single classifier. Ensemble based classifiers perform better than single classifier by either combining powers of multiple algorithms or introducing diversification to the same classifier by varying input in bot analysis. Our results are showing that by using voting method of ensemble based classifier accuracy is increased up to 96.41% from 93.37%.
Classical and Bayesian aspects of robust unit root inference
H. Hoek (Henk); H.K. van Dijk (Herman)
1995-01-01
textabstractThis paper has two themes. First, we classify some effects which outliers in the data have on unit root inference. We show that, both in a classical and a Bayesian framework, the presence of additive outliers moves ‘standard’ inference towards stationarity. Second, we base inference on a
Theory-Driven Science and Naive Empiricism in Counseling Psychology.
Strong, Stanley R.
1991-01-01
Asserts that counseling psychologists' aversion to theory-driven science and their enthusiasm for naive empiricism impede scientific progress. Identifies "received view" of science as theory-driven science, points out symptoms and consequences of the failure to apply this view, and argues that greater scientific progress will result from moving…
Thinking Process of Naive Problem Solvers to Solve Mathematical Problems
Mairing, Jackson Pasini
2017-01-01
Solving problems is not only a goal of mathematical learning. Students acquire ways of thinking, habits of persistence and curiosity, and confidence in unfamiliar situations by learning to solve problems. In fact, there were students who had difficulty in solving problems. The students were naive problem solvers. This research aimed to describe…
Children's Conceptions of Mental Illness: A Naive Theory Approach
Fox, Claudine; Buchanan-Barrow, Eithne; Barrett, Martyn
2010-01-01
This paper reports two studies that investigated children's conceptions of mental illness using a naive theory approach, drawing upon a conceptual framework for analysing illness representations which distinguishes between the identity, causes, consequences, curability, and timeline of an illness. The studies utilized semi-structured interviewing…
A Workshop for High School Students on Naive Set Theory
Wegner, Sven-Ake
2014-01-01
In this article we present the prototype of a workshop on naive set theory designed for high school students in or around the seventh year of primary education. Our concept is based on two events which the author organized in 2006 and 2010 for students of elementary school and high school, respectively. The article also includes a practice report…
Expert and Naive Raters Using the PAG: Does it Matter?
Cornelius, Edwin T.; And Others
1984-01-01
Questions the observed correlation between job experts and naive raters using the Position Analysis Questionnaire (PAQ); and conducts a replication of the Smith and Hakel study (1979) with college students (N=39). Concluded that PAQ ratings from job experts and college students are not equivalent and therefore are not interchangeable. (LLL)
Enzalutamide monotherapy in hormone-naive prostate cancer
DEFF Research Database (Denmark)
Tombal, Bertrand; Borre, Michael; Rathenborg, Per
2014-01-01
BACKGROUND: The androgen receptor inhibitor enzalutamide is approved for the treatment of metastatic castration-resistant prostate cancer that has progressed on docetaxel. Our aim was to assess the activity and safety of enzalutamide monotherapy in men with hormone-naive prostate cancer. METHODS:...
Naive Fault Trees for Safety Evaluations in Early Project Phase
Rajabali Nejad, Mohammadreza
2016-01-01
Naive Fault Trees (NFT) aim to extend the application of Fault Trees (FT) and make them appealing for system designers in the early project life cycle. NFT use input intervals and values to estimate the frequency of a top event. This extension facilitates the assignment of failure probability to
Bayesian Face Sketch Synthesis.
Wang, Nannan; Gao, Xinbo; Sun, Leiyu; Li, Jie
2017-03-01
Exemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical models. The proposed Bayesian framework consists of two parts: the neighbor selection model and the weight computation model. Within the proposed framework, we further propose a Bayesian face sketch synthesis method. The essential rationale behind the proposed Bayesian method is that we take the spatial neighboring constraint between adjacent image patches into consideration for both aforementioned models, while the state-of-the-art methods neglect the constraint either in the neighbor selection model or in the weight computation model. Extensive experiments on the Chinese University of Hong Kong face sketch database demonstrate that the proposed Bayesian method could achieve superior performance compared with the state-of-the-art methods in terms of both subjective perceptions and objective evaluations.
Center, Julian L.; Knuth, Kevin H.
2011-03-01
Visual odometry refers to tracking the motion of a body using an onboard vision system. Practical visual odometry systems combine the complementary accuracy characteristics of vision and inertial measurement units. The Mars Exploration Rovers, Spirit and Opportunity, used this type of visual odometry. The visual odometry algorithms in Spirit and Opportunity were based on Bayesian methods, but a number of simplifying approximations were needed to deal with onboard computer limitations. Furthermore, the allowable motion of the rover had to be severely limited so that computations could keep up. Recent advances in computer technology make it feasible to implement a fully Bayesian approach to visual odometry. This approach combines dense stereo vision, dense optical flow, and inertial measurements. As with all true Bayesian methods, it also determines error bars for all estimates. This approach also offers the possibility of using Micro-Electro Mechanical Systems (MEMS) inertial components, which are more economical, weigh less, and consume less power than conventional inertial components.
Hybrid Batch Bayesian Optimization
Azimi, Javad; Fern, Xiaoli
2012-01-01
Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function evaluations are possible. Under such a setting, BO could choose to either sequentially evaluate the function, one input at a time and wait for the output of the function before making the next selection, or evaluate the function at a batch of multiple inputs at once. These two different settings are commonly referred to as the sequential and batch settings of Bayesian Optimization. In general, the sequential setting leads to better optimization performance as each function evaluation is selected with more information, whereas the batch setting has an advantage in terms of the total experimental time (the number of iterations). In this work, our goal is to combine the strength of both settings. Specifically, we systematically analyze Bayesian optimization using Gaussian process as the posterior estimator and provide a hybrid algorithm t...
Bayesian least squares deconvolution
Asensio Ramos, A.; Petit, P.
2015-11-01
Aims: We develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods: We consider LSD under the Bayesian framework and we introduce a flexible Gaussian process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results: We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
Bayesian Exploratory Factor Analysis
DEFF Research Database (Denmark)
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.;
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corr......This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor......, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates...
Bayesian least squares deconvolution
Ramos, A Asensio
2015-01-01
Aims. To develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods. We consider LSD under the Bayesian framework and we introduce a flexible Gaussian Process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results. We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
Probabilistic Inferences in Bayesian Networks
Ding, Jianguo
2010-01-01
This chapter summarizes the popular inferences methods in Bayesian networks. The results demonstrates that the evidence can propagated across the Bayesian networks by any links, whatever it is forward or backward or intercausal style. The belief updating of Bayesian networks can be obtained by various available inference techniques. Theoretically, exact inferences in Bayesian networks is feasible and manageable. However, the computing and inference is NP-hard. That means, in applications, in ...
Bayesian Exploratory Factor Analysis
DEFF Research Database (Denmark)
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor......, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates...
Directory of Open Access Journals (Sweden)
Lee Youngbum
2011-10-01
Full Text Available Abstract Background The subjects in EEG-Brain computer interface (BCI system experience difficulties when attempting to obtain the consistent performance of the actual movement by motor imagery alone. It is necessary to find the optimal conditions and stimuli combinations that affect the performance factors of the EEG-BCI system to guarantee equipment safety and trust through the performance evaluation of using motor imagery characteristics that can be utilized in the EEG-BCI testing environment. Methods The experiment was carried out with 10 experienced subjects and 32 naive subjects on an EEG-BCI system. There were 3 experiments: The experienced homogeneous experiment, the naive homogeneous experiment and the naive heterogeneous experiment. Each experiment was compared in terms of the six audio-visual cue combinations and consisted of 50 trials. The EEG data was classified using the least square linear classifier in case of the naive subjects through the common spatial pattern filter. The accuracy was calculated using the training and test data set. The p-value of the accuracy was obtained through the statistical significance test. Results In the case in which a naive subject was trained by a heterogeneous combined cue and tested by a visual cue, the result was not only the highest accuracy (p Conclusions We propose the use of this measuring methodology of a heterogeneous combined cue for training data and a visual cue for test data by the typical EEG-BCI algorithm on the EEG-BCI system to achieve effectiveness in terms of consistence, stability, cost, time, and resources management without the need for a trial and error process.
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A non-parametric 2D deformable template classifier
DEFF Research Database (Denmark)
Schultz, Nette; Nielsen, Allan Aasbjerg; Conradsen, Knut;
2005-01-01
We introduce an interactive segmentation method for a sea floor survey. The method is based on a deformable template classifier and is developed to segment data from an echo sounder post-processor called RoxAnn. RoxAnn collects two different measures for each observation point, and in this 2D...... feature space the ship-master will be able to interactively define a segmentation map, which is refined and optimized by the deformable template algorithms. The deformable templates are defined as two-dimensional vector-cycles. Local random transformations are applied to the vector-cycles, and stochastic...... relaxation in a Bayesian scheme is used. In the Bayesian likelihood a class density function and its estimate hereof is introduced, which is designed to separate the feature space. The method is verified on data collected in Øresund, Scandinavia. The data come from four geographically different areas. Two...
Emergent behaviors of classifier systems
Energy Technology Data Exchange (ETDEWEB)
Forrest, S.; Miller, J.H.
1989-01-01
This paper discusses some examples of emergent behavior in classifier systems, describes some recently developed methods for studying them based on dynamical systems theory, and presents some initial results produced by the methodology. The goal of this work is to find techniques for noticing when interesting emergent behaviors of classifier systems emerge, to study how such behaviors might emerge over time, and make suggestions for designing classifier systems that exhibit preferred behaviors. 20 refs., 1 fig.
Bayesian methods for hackers probabilistic programming and Bayesian inference
Davidson-Pilon, Cameron
2016-01-01
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...
Trovato, Gabriele; Chrupala, Grzegorz; Takanishi, Atsuo
2016-01-01
As societies move towards integration of robots, it is important to study how robots can use their cognition in order to choose effectively their actions in a human environment, and possibly adapt to new contexts. When modelling these contextual data, it is common in social robotics to work with
The Bayesian Inventory Problem
1984-05-01
Bayesian Approach to Demand Estimation and Inventory Provisioning," Naval Research Logistics Quarterly. Vol 20, 1973, (p607-624). 4 DeGroot , Morris H...page is blank APPENDIX A SUFFICIENT STATISTICS A convenient reference for moat of this material is DeGroot (41. Su-pose that we are sampling from a
BNFinder2: Faster Bayesian network learning and Bayesian classification.
Dojer, Norbert; Bednarz, Pawel; Podsiadlo, Agnieszka; Wilczynski, Bartek
2013-08-15
Bayesian Networks (BNs) are versatile probabilistic models applicable to many different biological phenomena. In biological applications the structure of the network is usually unknown and needs to be inferred from experimental data. BNFinder is a fast software implementation of an exact algorithm for finding the optimal structure of the network given a number of experimental observations. Its second version, presented in this article, represents a major improvement over the previous version. The improvements include (i) a parallelized learning algorithm leading to an order of magnitude speed-ups in BN structure learning time; (ii) inclusion of an additional scoring function based on mutual information criteria; (iii) possibility of choosing the resulting network specificity based on statistical criteria and (iv) a new module for classification by BNs, including cross-validation scheme and classifier quality measurements with receiver operator characteristic scores. BNFinder2 is implemented in python and freely available under the GNU general public license at the project Web site https://launchpad.net/bnfinder, together with a user's manual, introductory tutorial and supplementary methods.
Acquiring a naive theory of kinship through inference.
Springer, K
1995-04-01
The present study focused on how children acquire a naive theory of kinship. Young children appear to have theoretical beliefs about the biological meaning of kinship relations. It was argued here that these beliefs reflect inductive inferences from simple facts about prenatal growth (e.g, where babies grow). An informal model of the inferences linking facts to theory was proposed and tested. In Experiment 1, 4-7-year-olds who knew the basic facts of prenatal growth were most likely to also express the naive theory of kinship. Virtually none of the children who expressed the theory were unaware of the basic facts. In Experiment 2, teaching the facts to a sample of preschoolers led to some increase in their acceptance of the kinship theory. Overall, the results implicate a type of theory building that involves inferences from preexisting knowledge rather than structural change, use of analogy, or acquisition of new knowledge.
Children's naive theories of intelligence influence their metacognitive judgments.
Miele, David B; Son, Lisa K; Metcalfe, Janet
2013-01-01
Recent studies have shown that the metacognitive judgments adults infer from their experiences of encoding effort vary in accordance with their naive theories of intelligence. To determine whether this finding extends to elementary schoolchildren, a study was conducted in which 27 third graders (M(age) = 8.27) and 24 fifth graders (M(age) = 10.39) read texts presented in easy- or difficult-to-encode fonts. The more children in both grades viewed intelligence as fixed, the less likely they were to interpret effortful or difficult encoding as a sign of increasing mastery and the more likely they were to report lower levels of comprehension as their perceived effort increased. This suggests that children may use naive theories of intelligence to make motivationally relevant inferences earlier than previously thought.
Feature Selection and Effective Classifiers.
Deogun, Jitender S.; Choubey, Suresh K.; Raghavan, Vijay V.; Sever, Hayri
1998-01-01
Develops and analyzes four algorithms for feature selection in the context of rough set methodology. Experimental results confirm the expected relationship between the time complexity of these algorithms and the classification accuracy of the resulting upper classifiers. When compared, results of upper classifiers perform better than lower…
Note---Naive Diversification and Portfolio Risk---A Note
Ron Bird; Mark Tippett
1986-01-01
A number of authors have used the portfolio standard deviation to model the risk reduction advantages of naive diversification. Other authors have pointed out that when risk is modelled by the portfolio's variance the modelling process becomes much simpler and is computationally more efficient. In this note we derive an exact parametric relationship between portfolio standard deviation and size and thus highlight the dangers of using the standard deviation in conjunction with O.L.S. regressio...
Sentiment Analysis of Movie Reviews using Hybrid Method of Naive Bayes and Genetic Algorithm
Directory of Open Access Journals (Sweden)
M.Govindarajan
2013-12-01
Full Text Available The area of sentiment mining (also called sentiment extraction, opinion mining, opinion extraction, sentiment analysis, etc. has seen a large increase in academic interest in the last few years. Researchers in the areas of natural language processing, data mining, machine learning, and others have tested a variety of methods of automating the sentiment analysis process. In this research work, new hybrid classification method is proposed based on coupling classification methods using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble was designed using Naive Bayes (NB, Genetic Algorithm (GA. In the proposed work, a comparative study of the effectiveness of ensemble technique is made for sentiment classification. The ensemble framework is applied to sentiment classification tasks, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more accurate classification procedure. The feasibility and the benefits of the proposed approaches are demonstrated by means of movie review that is widely used in the field of sentiment classification. A wide range of comparative experiments are conducted and finally, some in-depth discussion is presented and conclusions are drawn about the effectiveness of ensemble technique for sentiment classification.
Classifying supernovae using only galaxy data
Energy Technology Data Exchange (ETDEWEB)
Foley, Ryan J. [Astronomy Department, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801 (United States); Mandel, Kaisey [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States)
2013-12-01
We present a new method for probabilistically classifying supernovae (SNe) without using SN spectral or photometric data. Unlike all previous studies to classify SNe without spectra, this technique does not use any SN photometry. Instead, the method relies on host-galaxy data. We build upon the well-known correlations between SN classes and host-galaxy properties, specifically that core-collapse SNe rarely occur in red, luminous, or early-type galaxies. Using the nearly spectroscopically complete Lick Observatory Supernova Search sample of SNe, we determine SN fractions as a function of host-galaxy properties. Using these data as inputs, we construct a Bayesian method for determining the probability that an SN is of a particular class. This method improves a common classification figure of merit by a factor of >2, comparable to the best light-curve classification techniques. Of the galaxy properties examined, morphology provides the most discriminating information. We further validate this method using SN samples from the Sloan Digital Sky Survey and the Palomar Transient Factory. We demonstrate that this method has wide-ranging applications, including separating different subclasses of SNe and determining the probability that an SN is of a particular class before photometry or even spectra can. Since this method uses completely independent data from light-curve techniques, there is potential to further improve the overall purity and completeness of SN samples and to test systematic biases of the light-curve techniques. Further enhancements to the host-galaxy method, including additional host-galaxy properties, combination with light-curve methods, and hybrid methods, should further improve the quality of SN samples from past, current, and future transient surveys.
Beretvas, S. Natasha; Murphy, Daniel L.
2013-01-01
The authors assessed correct model identification rates of Akaike's information criterion (AIC), corrected criterion (AICC), consistent AIC (CAIC), Hannon and Quinn's information criterion (HQIC), and Bayesian information criterion (BIC) for selecting among cross-classified random effects models. Performance of default values for the 5…
Classifier-assisted metric for chromosome pairing.
Ventura, Rodrigo; Khmelinskii, Artem; Sanches, J
2010-01-01
Cytogenetics plays a central role in the detection of chromosomal abnormalities and in the diagnosis of genetic diseases. A karyogram is an image representation of human chromosomes arranged in order of decreasing size and paired in 23 classes. In this paper we propose an approach to automatically pair the chromosomes into a karyogram, using the information obtained in a rough SVM-based classification step, to help the pairing process mainly based on similarity metrics between the chromosomes. Using a set of geometric and band pattern features extracted from the chromosome images, the algorithm is formulated on a Bayesian framework, combining the similarity metric with the results from the classifier. The solution is obtained solving a mixed integer program. Two datasets with contrasting quality levels and 836 chromosomes each were used to test and validate the algorithm. Relevant improvements with respect to the algorithm described by the authors in [1] were obtained with average paring rates above 92%, close to the rates obtained by human operators.
Sampling Based Average Classifier Fusion
Directory of Open Access Journals (Sweden)
Jian Hou
2014-01-01
fusion algorithms have been proposed in literature, average fusion is almost always selected as the baseline for comparison. Little is done on exploring the potential of average fusion and proposing a better baseline. In this paper we empirically investigate the behavior of soft labels and classifiers in average fusion. As a result, we find that; by proper sampling of soft labels and classifiers, the average fusion performance can be evidently improved. This result presents sampling based average fusion as a better baseline; that is, a newly proposed classifier fusion algorithm should at least perform better than this baseline in order to demonstrate its effectiveness.
Computer Security Team
2011-01-01
In the last issue of the Bulletin, we have discussed recent implications for privacy on the Internet. But privacy of personal data is just one facet of data protection. Confidentiality is another one. However, confidentiality and data protection are often perceived as not relevant in the academic environment of CERN. But think twice! At CERN, your personal data, e-mails, medical records, financial and contractual documents, MARS forms, group meeting minutes (and of course your password!) are all considered to be sensitive, restricted or even confidential. And this is not all. Physics results, in particular when being preliminary and pending scrutiny, are sensitive, too. Just recently, an ATLAS collaborator copy/pasted the abstract of an ATLAS note onto an external public blog, despite the fact that this document was clearly marked as an "Internal Note". Such an act was not only embarrassing to the ATLAS collaboration, and had negative impact on CERN’s reputation --- i...
Probability and Bayesian statistics
1987-01-01
This book contains selected and refereed contributions to the "Inter national Symposium on Probability and Bayesian Statistics" which was orga nized to celebrate the 80th birthday of Professor Bruno de Finetti at his birthplace Innsbruck in Austria. Since Professor de Finetti died in 1985 the symposium was dedicated to the memory of Bruno de Finetti and took place at Igls near Innsbruck from 23 to 26 September 1986. Some of the pa pers are published especially by the relationship to Bruno de Finetti's scientific work. The evolution of stochastics shows growing importance of probability as coherent assessment of numerical values as degrees of believe in certain events. This is the basis for Bayesian inference in the sense of modern statistics. The contributions in this volume cover a broad spectrum ranging from foundations of probability across psychological aspects of formulating sub jective probability statements, abstract measure theoretical considerations, contributions to theoretical statistics an...
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel N
2012-01-01
Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....
Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6
Directory of Open Access Journals (Sweden)
Dan B. Jensen
2013-09-01
Full Text Available Background: Prediction of the optimal habitat conditions for a given bacterium, based on genome sequence alone would be of value for scientific as well as industrial purposes. One example of such a habitat adaptation is the requirement for oxygen. In spite of good genome data availability, there have been only a few prediction attempts of bacterial oxygen requirements, using genome sequences. Here, we describe a method for distinguishing aerobic, anaerobic and facultative anaerobic bacteria, based on genome sequence-derived input, using naive Bayesian inference. In contrast, other studies found in literature only demonstrate the ability to distinguish two classes at a time. Results: The results shown in the present study are as good as or better than comparable methods previously described in the scientific literature, with an arguably simpler method, when results are directly compared. This method further compares the performance of a single-step naive Bayesian prediction of the three included classifications, compared to a simple Bayesian network with two steps. A two-step network, distinguishing first respiring from non-respiring organisms, followed by the distinction of aerobe and facultative anaerobe organisms within the respiring group, is found to perform best. Conclusions: A simple naive Bayesian network based on the presence or absence of specific protein domains within a genome is an effective and easy way to predict bacterial habitat preferences, such as oxygen requirement.
Enhancing atlas based segmentation with multiclass linear classifiers
Energy Technology Data Exchange (ETDEWEB)
Sdika, Michaël, E-mail: michael.sdika@creatis.insa-lyon.fr [Université de Lyon, CREATIS, CNRS UMR 5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Villeurbanne 69300 (France)
2015-12-15
Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible local registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.
Introduction to Bayesian statistics
Koch, Karl-Rudolf
2007-01-01
This book presents Bayes' theorem, the estimation of unknown parameters, the determination of confidence regions and the derivation of tests of hypotheses for the unknown parameters. It does so in a simple manner that is easy to comprehend. The book compares traditional and Bayesian methods with the rules of probability presented in a logical way allowing an intuitive understanding of random variables and their probability distributions to be formed.
Shterev, Ivo; Dunson, David
2012-01-01
This paper presents an application of statistical machine learning to the field of watermarking. We propose a new attack model on additive spread-spectrum watermarking systems. The proposed attack is based on Bayesian statistics. We consider the scenario in which a watermark signal is repeatedly embedded in specific, possibly chosen based on a secret message bitstream, segments (signals) of the host data. The host signal can represent a patch of pixels from an image or a video frame. We propo...
DEFF Research Database (Denmark)
Antoniou, Constantinos; Harrison, Glenn W.; Lau, Morten I.
2015-01-01
A large literature suggests that many individuals do not apply Bayes’ Rule when making decisions that depend on them correctly pooling prior information and sample data. We replicate and extend a classic experimental study of Bayesian updating from psychology, employing the methods of experimental...... economics, with careful controls for the confounding effects of risk aversion. Our results show that risk aversion significantly alters inferences on deviations from Bayes’ Rule....
Himpe, Christian; Ohlberger, Mario
2014-01-01
Bayesian inversion of models with large state and parameter spaces proves to be computationally complex. A combined state and parameter reduction can significantly decrease the computational time and cost required for the parameter estimation. The presented technique is based on the well-known balanced truncation approach. Classically, the balancing of the controllability and observability gramians allows a truncation of discardable states. Here the underlying model, being a linear or nonline...
Bayesian Independent Component Analysis
DEFF Research Database (Denmark)
Winther, Ole; Petersen, Kaare Brandt
2007-01-01
In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...... in a Matlab toolbox, is demonstrated for non-negative decompositions and compared with non-negative matrix factorization....
Optimally Training a Cascade Classifier
Shen, Chunhua; Hengel, Anton van den
2010-01-01
Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object detection, there is no a principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such an algorithm here. We show a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of \\cite{wu2005linear}. We then design a new boosting algorithm that directly optimizes the cost function of LAC. The resulting totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on object detection verify the effectiveness of the proposed bo...
Bayesian theory and applications
Dellaportas, Petros; Polson, Nicholas G; Stephens, David A
2013-01-01
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...
Combining different types of classifiers
Gatnar, Eugeniusz
2008-01-01
Model fusion has proved to be a very successful strategy for obtaining accurate models in classification and regression. The key issue, however, is the diversity of the component classifiers because classification error of an ensemble depends on the correlation between its members. The majority of existing ensemble methods combine the same type of models, e.g. trees. In order to promote the diversity of the ensemble members, we propose to aggregate classifiers of different t...
Aerial Image Texture Classification Based on u-level Bayesian Network%多级Bayesian Network的影像纹理分类方法
Institute of Scientific and Technical Information of China (English)
虞欣; 郑肇葆; 叶志伟; 李林宜
2008-01-01
在影像分类的实际应用中,所提取的特征(或波段)间往往存在较大的相关性.为了把Naive Bayes Classifters (NBC)模型更好地应用于分类中,本文在研究NBC模型的基础上,从特征空间划分的角度,将它进一步推广为多级Bayesian Network.实验结果分析表明:由于多级Bayesian Network模型综合考虑了特征之间的条件依赖关系,它在分类精度方面一般高于原始的NBC和最大似然法.然而,对于不同的n值,其分类结果也有所不同.
Optimal weighted nearest neighbour classifiers
Samworth, Richard J
2011-01-01
We derive an asymptotic expansion for the excess risk (regret) of a weighted nearest-neighbour classifier. This allows us to find the asymptotically optimal vector of non-negative weights, which has a rather simple form. We show that the ratio of the regret of this classifier to that of an unweighted $k$-nearest neighbour classifier depends asymptotically only on the dimension $d$ of the feature vectors, and not on the underlying population densities. The improvement is greatest when $d=4$, but thereafter decreases as $d \\rightarrow \\infty$. The popular bagged nearest neighbour classifier can also be regarded as a weighted nearest neighbour classifier, and we show that its corresponding weights are somewhat suboptimal when $d$ is small (in particular, worse than those of the unweighted $k$-nearest neighbour classifier when $d=1$), but are close to optimal when $d$ is large. Finally, we argue that improvements in the rate of convergence are possible under stronger smoothness assumptions, provided we allow nega...
Neonatal thymectomy reveals differentiation and plasticity within human naive T cells
van den Broek, Theo; Delemarre, Eveline M.; Janssen, Willemijn J.M.; Nievelstein, Rutger A.J.; Broen, Jasper C.; Tesselaar, Kiki; Borghans, Jose A.M.; Nieuwenhuis, Edward E.S.; Prakken, Berent J.; Mokry, Michal; Jansen, Nicolaas J.G.
2016-01-01
The generation of naive T cells is dependent on thymic output, but in adults, the naive T cell pool is primarily maintained by peripheral proliferation. Naive T cells have long been regarded as relatively quiescent cells; however, it was recently shown that IL-8 production is a signatory effector function of naive T cells, at least in newborns. How this functional signature relates to naive T cell dynamics and aging is unknown. Using a cohort of children and adolescents who underwent neonatal thymectomy, we demonstrate that the naive CD4+ T cell compartment in healthy humans is functionally heterogeneous and that this functional diversity is lost after neonatal thymectomy. Thymic tissue regeneration later in life resulted in functional restoration of the naive T cell compartment, implicating the thymus as having functional regenerative capacity. Together, these data shed further light on functional differentiation within the naive T cell compartment and the importance of the thymus in human naive T cell homeostasis and premature aging. In addition, these results affect and alter our current understanding on the identification of truly naive T cells and recent thymic emigrants. PMID:26901814
Hybrid classifiers methods of data, knowledge, and classifier combination
Wozniak, Michal
2014-01-01
This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.
Sensory Adaptation in Naive Peripheral CD4 T Cells
Smith, Katy; Seddon, Benedict; Purbhoo, Marco A.; Zamoyska, Rose; Fisher, Amanda G.; Merkenschlager, Matthias
2001-01-01
T cell receptor interactions with peptide/major histocompatibility complex (pMHC) ligands control the selection of T cells in the thymus as well as their homeostasis in peripheral lymphoid organs. Here we show that pMHC contact modulates the expression of CD5 by naive CD4 T cells in a process that requires the continued expression of p56lck. Reduced CD5 levels in T cells deprived of pMHC contact are predictive of elevated Ca2+ responses to subsequent TCR engagement by anti-CD3 or nominal anti...
A bayesian approach to classification criteria for spectacled eiders
Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.
1996-01-01
To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.
A Hybrid Generative/Discriminative Classifier Design for Semi-supervised Learing
Fujino, Akinori; Ueda, Naonori; Saito, Kazumi
Semi-supervised classifier design that simultaneously utilizes both a small number of labeled samples and a large number of unlabeled samples is a major research issue in machine learning. Existing semi-supervised learning methods for probabilistic classifiers belong to either generative or discriminative approaches. This paper focuses on a semi-supervised probabilistic classifier design for multiclass and single-labeled classification problems and first presents a hybrid approach to take advantage of the generative and discriminative approaches. Our formulation considers a generative model trained on labeled samples and a newly introduced bias correction model, whose belongs to the same model family as the generative model, but whose parameters are different from the generative model. A hybrid classifier is constructed by combining both the generative and bias correction models based on the maximum entropy principle, where the combination weights of these models are determined so that the class labels of labeled samples are as correctly predicted as possible. We apply the hybrid approach to text classification problems by employing naive Bayes as the generative and bias correction models. In our experimental results on three English and one Japanese text data sets, we confirmed that the hybrid classifier significantly outperformed conventional probabilistic generative and discriminative classifiers when the classification performance of the generative classifier was comparable to the discriminative classifier.
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Congdon, Peter
2014-01-01
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBU
DEFF Research Database (Denmark)
Hartelius, Karsten; Carstensen, Jens Michael
2003-01-01
A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov random field (MRF) model which...... nodes and the arc prior models variations in row and column spacing across the grid. Grid matching is done by placing an initial rough grid over the image and applying an ensemble annealing scheme to maximize the posterior distribution of the grid. The method can be applied to noisy images with missing...
Using predictive distributions to estimate uncertainty in classifying landmine targets
Close, Ryan; Watford, Ken; Glenn, Taylor; Gader, Paul; Wilson, Joseph
2011-06-01
Typical classification models used for detection of buried landmines estimate a singular discriminative output. This classification is based on a model or technique trained with a given set of training data available during system development. Regardless of how well the technique performs when classifying objects that are 'similar' to the training set, most models produce undesirable (and many times unpredictable) responses when presented with object classes different from the training data. This can cause mines or other explosive objects to be misclassified as clutter, or false alarms. Bayesian regression and classification models produce distributions as output, called the predictive distribution. This paper will discuss predictive distributions and their application to characterizing uncertainty in the classification decision, from the context of landmine detection. Specifically, experiments comparing the predictive variance produced by relevance vector machines and Gaussian processes will be described. We demonstrate that predictive variance can be used to determine the uncertainty of the model in classifying an object (i.e., the classifier will know when it's unable to reliably classify an object). The experimental results suggest that degenerate covariance models (such as the relevance vector machine) are not reliable in estimating the predictive variance. This necessitates the use of the Gaussian Process in creating the predictive distribution.
Directory of Open Access Journals (Sweden)
Tobias Nef
2015-05-01
Full Text Available Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB, support vector machine (SVM and random forest (RF. Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different
Nef, Tobias; Urwyler, Prabitha; Büchler, Marcel; Tarnanas, Ioannis; Stucki, Reto; Cazzoli, Dario; Müri, René; Mosimann, Urs
2015-05-21
Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our
Bayesian Approach for Inconsistent Information.
Stein, M; Beer, M; Kreinovich, V
2013-10-01
In engineering situations, we usually have a large amount of prior knowledge that needs to be taken into account when processing data. Traditionally, the Bayesian approach is used to process data in the presence of prior knowledge. Sometimes, when we apply the traditional Bayesian techniques to engineering data, we get inconsistencies between the data and prior knowledge. These inconsistencies are usually caused by the fact that in the traditional approach, we assume that we know the exact sample values, that the prior distribution is exactly known, etc. In reality, the data is imprecise due to measurement errors, the prior knowledge is only approximately known, etc. So, a natural way to deal with the seemingly inconsistent information is to take this imprecision into account in the Bayesian approach - e.g., by using fuzzy techniques. In this paper, we describe several possible scenarios for fuzzifying the Bayesian approach. Particular attention is paid to the interaction between the estimated imprecise parameters. In this paper, to implement the corresponding fuzzy versions of the Bayesian formulas, we use straightforward computations of the related expression - which makes our computations reasonably time-consuming. Computations in the traditional (non-fuzzy) Bayesian approach are much faster - because they use algorithmically efficient reformulations of the Bayesian formulas. We expect that similar reformulations of the fuzzy Bayesian formulas will also drastically decrease the computation time and thus, enhance the practical use of the proposed methods.
Classification using Bayesian neural nets
J.C. Bioch (Cor); O. van der Meer; R. Potharst (Rob)
1995-01-01
textabstractRecently, Bayesian methods have been proposed for neural networks to solve regression and classification problems. These methods claim to overcome some difficulties encountered in the standard approach such as overfitting. However, an implementation of the full Bayesian approach to neura
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....
Interactive Instruction in Bayesian Inference
DEFF Research Database (Denmark)
Khan, Azam; Breslav, Simon; Hornbæk, Kasper
2017-01-01
An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....
Bayesian Intersubjectivity and Quantum Theory
Pérez-Suárez, Marcos; Santos, David J.
2005-02-01
Two of the major approaches to probability, namely, frequentism and (subjectivistic) Bayesian theory, are discussed, together with the replacement of frequentist objectivity for Bayesian intersubjectivity. This discussion is then expanded to Quantum Theory, as quantum states and operations can be seen as structural elements of a subjective nature.
Classifying Cereal Data (Earlier Methods)
The DSQ includes questions about cereal intake and allows respondents up to two responses on which cereals they consume. We classified each cereal reported first by hot or cold, and then along four dimensions: density of added sugars, whole grains, fiber, and calcium.
Bayesian inference with ecological applications
Link, William A
2009-01-01
This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...
Bayesian Inference on Gravitational Waves
Directory of Open Access Journals (Sweden)
Asad Ali
2015-12-01
Full Text Available The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.
Approximate Bayesian computation.
Directory of Open Access Journals (Sweden)
Mikael Sunnåker
Full Text Available Approximate Bayesian computation (ABC constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology.
Cai, Baoping; Liu, Yonghong; Liu, Zengkai; Tian, Xiaojie; Zhang, Yanzhen; Ji, Renjie
2013-07-01
This article proposes a methodology for the application of Bayesian networks in conducting quantitative risk assessment of operations in offshore oil and gas industry. The method involves translating a flow chart of operations into the Bayesian network directly. The proposed methodology consists of five steps. First, the flow chart is translated into a Bayesian network. Second, the influencing factors of the network nodes are classified. Third, the Bayesian network for each factor is established. Fourth, the entire Bayesian network model is established. Lastly, the Bayesian network model is analyzed. Subsequently, five categories of influencing factors, namely, human, hardware, software, mechanical, and hydraulic, are modeled and then added to the main Bayesian network. The methodology is demonstrated through the evaluation of a case study that shows the probability of failure on demand in closing subsea ram blowout preventer operations. The results show that mechanical and hydraulic factors have the most important effects on operation safety. Software and hardware factors have almost no influence, whereas human factors are in between. The results of the sensitivity analysis agree with the findings of the quantitative analysis. The three-axiom-based analysis partially validates the correctness and rationality of the proposed Bayesian network model.
Bayesian Image Classification At High Latitudes
Bulgin, Claire E.; Eastwood, Steinar; Merchant, Chris J.
2013-12-01
The European Space Agency created the Climate Change Initiative (CCI) to maximize the usefulness of Earth Observations to climate science. Sea Surface Temperature (SST) is an essential climate variable to which satellite observations make a crucial contribution, and is one of the projects within the CCI program. SST retrieval is dependent on successful cloud clearing and identification of clear-sky pixels over ocean. At high latitudes image classification is more difficult due to the presence of sea-ice. Newly formed ice has a temperature close to the freezing point of water and a dark surface making it difficult to distinguish from open ocean using data at visible and infrared wavelengths. Similarly, melt ponds on the sea-ice surface make image classification more difficult. We present here a three- way Bayesian classifier for the AATSR instrument classifying pixels as ‘clear-sky over ocean', ‘clear-sky over ice' or ‘cloud' using the 0.6, 1.6, 11 and 12 micron channels. We demonstrate the ability of the classifier to successfully identify sea-ice and consider the potential for generating an ice surface temperature record from AATSR which could be extended using data from SLSTR.
Borsboom, D.; Haig, B.D.
2013-01-01
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science primar
Implementing Bayesian Vector Autoregressions Implementing Bayesian Vector Autoregressions
Directory of Open Access Journals (Sweden)
Richard M. Todd
1988-03-01
Full Text Available Implementing Bayesian Vector Autoregressions This paper discusses how the Bayesian approach can be used to construct a type of multivariate forecasting model known as a Bayesian vector autoregression (BVAR. In doing so, we mainly explain Doan, Littermann, and Sims (1984 propositions on how to estimate a BVAR based on a certain family of prior probability distributions. indexed by a fairly small set of hyperparameters. There is also a discussion on how to specify a BVAR and set up a BVAR database. A 4-variable model is used to iliustrate the BVAR approach.
Classifying self-gravitating radiations
Kim, Hyeong-Chan
2016-01-01
We study static systems of self-gravitating radiations confined in a sphere by using numerical and analytic calculations. We classify and analyze the solutions systematically. Due to the scaling symmetry, any solution can be represented as a segment of a solution curve on a plane of two-dimensional scale invariant variables. We find that a system can be conveniently parametrized by three parameters representing the solution curve, the scaling, and the system size, instead of the parameters defined at the outer boundary. The solution curves are classified to three types representing regular solutions, conically singular solutions with, and without an object which resembles an event horizon up to causal disconnectedness. For the last type, the behavior of a self-gravitating system is simple enough to allow analytic calculations.
Modeling Social Annotation: a Bayesian Approach
Plangprasopchok, Anon
2008-01-01
Collaborative tagging systems, such as del.icio.us, CiteULike, and others, allow users to annotate objects, e.g., Web pages or scientific papers, with descriptive labels called tags. The social annotations, contributed by thousands of users, can potentially be used to infer categorical knowledge, classify documents or recommend new relevant information. Traditional text inference methods do not make best use of socially-generated data, since they do not take into account variations in individual users' perspectives and vocabulary. In a previous work, we introduced a simple probabilistic model that takes interests of individual annotators into account in order to find hidden topics of annotated objects. Unfortunately, our proposed approach had a number of shortcomings, including overfitting, local maxima and the requirement to specify values for some parameters. In this paper we address these shortcomings in two ways. First, we extend the model to a fully Bayesian framework. Second, we describe an infinite ver...
76 FR 34761 - Classified National Security Information
2011-06-14
... Classified National Security Information AGENCY: Marine Mammal Commission. ACTION: Notice. SUMMARY: This... information, as directed by Information Security Oversight Office regulations. FOR FURTHER INFORMATION CONTACT..., ``Classified National Security Information,'' and 32 CFR part 2001, ``Classified National Security......
Energy-Efficient Neuromorphic Classifiers.
Martí, Daniel; Rigotti, Mattia; Seok, Mingoo; Fusi, Stefano
2016-10-01
Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.
Left ventricular function in treatment-naive early rheumatoid arthritis
DEFF Research Database (Denmark)
Løgstrup, Brian B; Deibjerg, Lone K; Hedemann-Andersen, Agnete;
2014-01-01
BACKGROUND: The role of inflammation and anti-cyclic citrullinated peptide antibodies (anti-CCP) in the pathogenesis of cardiovascular disease in early rheumatoid arthritis (RA) remains unclear. Previous studies have suggested that both disease activity and disease duration are associated...... with atherosclerosis and a higher mortality rate caused primarily by coronary artery disease. OBJECTIVE: We investigated how disease activity, anti-CCP status and coronary calcium score in treatment-naive early RA impacts left ventricular (LV) systolic function. METHODS: Fifty-tree patients (30 women) with mean age 58...... by computed tomography by calculating the Agaston score. One experienced senior rheumatologist and one experienced cardiologist performed all the clinical assessments as well as all the transthoracic echocardiography (TTE) and coronary CT analysis. RESULTS: Disease activity scores before treatment at baseline...
Reduced error signalling in medication-naive children with ADHD
DEFF Research Database (Denmark)
Plessen, Kerstin J; Allen, Elena A; Eichele, Heike;
2016-01-01
and correlated them with reaction times (RT). Additionally, we analyzed post-error adaptations in behaviour and motor component activations. RESULTS: We included 25 children with ADHD and 29 controls in our analysis. Children with ADHD displayed reduced activation to errors in cingulo-opercular regions......BACKGROUND: We examined the blood-oxygen level-dependent (BOLD) activation in brain regions that signal errors and their association with intraindividual behavioural variability and adaptation to errors in children with attention-deficit/hyperactivity disorder (ADHD). METHODS: We acquired...... functional MRI data during a Flanker task in medication-naive children with ADHD and healthy controls aged 8-12 years and analyzed the data using independent component analysis. For components corresponding to performance monitoring networks, we compared activations across groups and conditions...
Reduced error signalling in medication-naive children with ADHD
DEFF Research Database (Denmark)
Plessen, Kerstin J; Allen, Elena A; Eichele, Heike
2016-01-01
functional MRI data during a Flanker task in medication-naive children with ADHD and healthy controls aged 8-12 years and analyzed the data using independent component analysis. For components corresponding to performance monitoring networks, we compared activations across groups and conditions......BACKGROUND: We examined the blood-oxygen level-dependent (BOLD) activation in brain regions that signal errors and their association with intraindividual behavioural variability and adaptation to errors in children with attention-deficit/hyperactivity disorder (ADHD). METHODS: We acquired...... and correlated them with reaction times (RT). Additionally, we analyzed post-error adaptations in behaviour and motor component activations. RESULTS: We included 25 children with ADHD and 29 controls in our analysis. Children with ADHD displayed reduced activation to errors in cingulo-opercular regions...
Professional Stereotypes of Interprofessional Education Naive Pharmacy and Nursing Students
Thurston, Maria Miller; Harris, Elaine C.; Ryan, Gina J.
2017-01-01
Objective. To assess and compare interprofessional education (IPE) naive pharmacy and nursing student stereotypes prior to completion of an IPE activity. Methods. Three hundred and twenty-three pharmacy students and 275 nursing students at Mercer University completed the Student Stereotypes Rating Questionnaire. Responses from pharmacy and nursing students were compared, and responses from different level learners within the same profession also were compared. Results. Three hundred and fifty-six (59.5%) students completed the survey. Pharmacy students viewed pharmacists more favorably than nursing students viewed pharmacists for all attributes except the ability to work independently. Additionally, nursing students viewed nurses less favorably than pharmacy students viewed nurses for academic ability and practical skills. There was some variability in stereotypes between professional years. Conclusion. This study confirms the existence of professional stereotypes, although overall student perceptions of their own profession and the other were generally positive. PMID:28720912
Naive T cells are resistant to anergy induction by anti-CD3 antibodies.
Andris, F.; Denanglaire, S.; Mattia, F.P. de; Urbain, J.; Leo, O.
2004-01-01
Anti-CD3 mAbs are potent immunosuppressive agents used in clinical transplantation. It has been generally assumed that one of the anti-CD3 mAb-mediated tolerance mechanisms is through the induction of naive T cell unresponsiveness, often referred to as anergy. We demonstrate in this study that naive
Bayesian inference in geomagnetism
Backus, George E.
1988-01-01
The inverse problem in empirical geomagnetic modeling is investigated, with critical examination of recently published studies. Particular attention is given to the use of Bayesian inference (BI) to select the damping parameter lambda in the uniqueness portion of the inverse problem. The mathematical bases of BI and stochastic inversion are explored, with consideration of bound-softening problems and resolution in linear Gaussian BI. The problem of estimating the radial magnetic field B(r) at the earth core-mantle boundary from surface and satellite measurements is then analyzed in detail, with specific attention to the selection of lambda in the studies of Gubbins (1983) and Gubbins and Bloxham (1985). It is argued that the selection method is inappropriate and leads to lambda values much larger than those that would result if a reasonable bound on the heat flow at the CMB were assumed.
Bayesian Geostatistical Design
DEFF Research Database (Denmark)
Diggle, Peter; Lophaven, Søren Nymand
2006-01-01
This paper describes the use of model-based geostatistics for choosing the set of sampling locations, collectively called the design, to be used in a geostatistical analysis. Two types of design situation are considered. These are retrospective design, which concerns the addition of sampling...... locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model...... parameter values are unknown. The results show that in this situation a wide range of interpoint distances should be included in the design, and the widely used regular design is often not the best choice....
Blundell, Charles; Heller, Katherine A
2012-01-01
Hierarchical structure is ubiquitous in data across many domains. There are many hier- archical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these meth- ods limit discoverable hierarchies to those with binary branching structure. This lim- itation, while computationally convenient, is often undesirable. In this paper we ex- plore a Bayesian hierarchical clustering algo- rithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy ag- glomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.
Ortega, Pedro A
2011-01-01
Discovering causal relationships is a hard task, often hindered by the need for intervention, and often requiring large amounts of data to resolve statistical uncertainty. However, humans quickly arrive at useful causal relationships. One possible reason is that humans use strong prior knowledge; and rather than encoding hard causal relationships, they encode beliefs over causal structures, allowing for sound generalization from the observations they obtain from directly acting in the world. In this work we propose a Bayesian approach to causal induction which allows modeling beliefs over multiple causal hypotheses and predicting the behavior of the world under causal interventions. We then illustrate how this method extracts causal information from data containing interventions and observations.
Book review: Bayesian analysis for population ecology
Link, William A.
2011-01-01
Brian Dennis described the field of ecology as “fertile, uncolonized ground for Bayesian ideas.” He continued: “The Bayesian propagule has arrived at the shore. Ecologists need to think long and hard about the consequences of a Bayesian ecology. The Bayesian outlook is a successful competitor, but is it a weed? I think so.” (Dennis 2004)
Aggregation Operator Based Fuzzy Pattern Classifier Design
DEFF Research Database (Denmark)
Mönks, Uwe; Larsen, Henrik Legind
2009-01-01
This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable. The perfor......This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable...
Using an Integrated Naive Bayes Calssifier for Crawling Relevent Data on the Web
Mihsra, A.
2015-12-01
In our experiments (at JPL, NASA) for DARPA Memex project, we wanted to crawl a large amount of data for various domains. A big challenge was data relevancy in the crawled data. More than 50% of the data was irrelevant to the domain at hand. One immediate solution was to use good seeds (seeds are the initial urls from where the program starts to crawl) and make sure that the crawl remains into the original host urls. This although a very efficient technique, fails under two conditions. One when you aim to reach deeper into the web; into new hosts (not in the seed list) and two when the website hosts myriad content types eg. a News website.The relevancy calculation used to be a post processing step i.e. once we had finished crawling, we trained a NaiveBayes Classifier and used it to find a rough relevancy of the web pages that we had. Integrating the relevancy into the crawling rather than after it was very important because crawling takes resources and time. To save both we needed to get an idea of relevancy of the whole crawl during run time and be able to steer its course accordingly. We use Apache Nutch as the crawler, which uses a plugin system to incorporate any new implementations and hence we built a plugin for Nutch.The Naive Bayes Parse Plugin works in the following way. It parses every page and decides, using a trained model (which is built in situ only once using the positive and negative examples given by the user in a very simple format), if it is relevant; If true, then it allows all the outlinks from that page to go to the next round of crawling; If not, then it gives the urls a second chance to prove themselves by checking some commonly expected words in the url relevant to that domain. This two tier system is very intuitive and efficient in focusing the crawl. In our initial test experiments over 100 seed urls, the results were astonishingly good with a recall of 98%.The same technique can be applied to geo-informatics. This will help scientists
Bayesian adaptive methods for clinical trials
Berry, Scott M; Muller, Peter
2010-01-01
Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISP...
Current trends in Bayesian methodology with applications
Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia
2015-01-01
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on
GS-TEC: the Gaia Spectrophotometry Transient Events Classifier
Blagorodnova, Nadejda; Wyrzykowski, \\Lukasz; Irwin, Mike; Walton, Nicholas A
2014-01-01
We present an algorithm for classifying the nearby transient objects detected by the Gaia satellite. The algorithm will use the low-resolution spectra from the blue and red spectro-photometers on board of the satellite. Taking a Bayesian approach we model the spectra using the newly constructed reference spectral library and literature-driven priors. We find that for magnitudes brighter than 19 in Gaia $G$ magnitude, around 75\\% of the transients will be robustly classified. The efficiency of the algorithm for SNe type I is higher than 80\\% for magnitudes $G\\leq$18, dropping to approximately 60\\% at magnitude $G$=19. For SNe type II, the efficiency varies from 75 to 60\\% for $G\\leq$18, falling to 50\\% at $G$=19. The purity of our classifier is around 95\\% for SNe type I for all magnitudes. For SNe type II it is over 90\\% for objects with $G \\leq$19. GS-TEC also estimates the redshifts with errors of $\\sigma_z \\le$ 0.01 and epochs with uncertainties $\\sigma_t \\simeq$ 13 and 32 days for type SNe I and SNe II re...
Classifier-Guided Sampling for Complex Energy System Optimization
Energy Technology Data Exchange (ETDEWEB)
Backlund, Peter B. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
2015-09-01
This report documents the results of a Laboratory Directed Research and Development (LDRD) effort enti tled "Classifier - Guided Sampling for Complex Energy System Optimization" that was conducted during FY 2014 and FY 2015. The goal of this proj ect was to develop, implement, and test major improvements to the classifier - guided sampling (CGS) algorithm. CGS is type of evolutionary algorithm for perform ing search and optimization over a set of discrete design variables in the face of one or more objective functions. E xisting evolutionary algorithms, such as genetic algorithms , may require a large number of o bjecti ve function evaluations to identify optimal or near - optimal solutions . Reducing the number of evaluations can result in significant time savings, especially if the objective function is computationally expensive. CGS reduce s the evaluation count by us ing a Bayesian network classifier to filter out non - promising candidate designs , prior to evaluation, based on their posterior probabilit ies . In this project, b oth the single - objective and multi - objective version s of the CGS are developed and tested on a set of benchm ark problems. As a domain - specific case study, CGS is used to design a microgrid for use in islanded mode during an extended bulk power grid outage.
Defining and Classifying Interest Groups
DEFF Research Database (Denmark)
Baroni, Laura; Carroll, Brendan; Chalmers, Adam;
2014-01-01
The interest group concept is defined in many different ways in the existing literature and a range of different classification schemes are employed. This complicates comparisons between different studies and their findings. One of the important tasks faced by interest group scholars engaged...... in large-N studies is therefore to define the concept of an interest group and to determine which classification scheme to use for different group types. After reviewing the existing literature, this article sets out to compare different approaches to defining and classifying interest groups with a sample...
Fingerprint prediction using classifier ensembles
CSIR Research Space (South Africa)
Molale, P
2011-11-01
Full Text Available -based learning algorithms. Machine Learning, 6: pp: 37-66. Amit, Y., D. Geman, and K. Wilder, 1997. Joint Induction of Shape Features and Tree Classifiers. IEEE Transc. on Pattern Anal. and machine Intell., 19 (11), pp: 1300- 1305. Breiman, L., 1996. Bagging.... NIST Technical Report NISTIR 5163. Cappelli, R., A. Lumini, D. Maio., and D. Maltoni, 1999. Fingerprint Classification by Direct image Partitioning. IEEE Transc. On Pattern Anal. and Machine Intell., 21 (5), pp: 402-421. Cox, D.R., 1966. Some...
Irregular-Time Bayesian Networks
Ramati, Michael
2012-01-01
In many fields observations are performed irregularly along time, due to either measurement limitations or lack of a constant immanent rate. While discrete-time Markov models (as Dynamic Bayesian Networks) introduce either inefficient computation or an information loss to reasoning about such processes, continuous-time Markov models assume either a discrete state space (as Continuous-Time Bayesian Networks), or a flat continuous state space (as stochastic dif- ferential equations). To address these problems, we present a new modeling class called Irregular-Time Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, allowing substantially more compact representations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learning temporal systems, provided that they are fully observed at the same irregularly spaced time-points, and a semiparametric subclass of ITBNs is introduced to allow further adaptation to the irregular nature of t...
Comparision of methods for combination of multiple classifiers that predict behavior patterns
Directory of Open Access Journals (Sweden)
Anuja V. Deshpande
2014-09-01
Full Text Available Predictive analysis include techniques fromdata mining that analyze current and historical data and make predictions about the future. Predictive analytics is used in actuarial science, financial services, retail, travel, healthcare, insurance, pharmaceuticals, marketing, telecommunications and other fields.Predicting patterns can be considered as a classification problem and combining the different classifiers gives better results. We will study and compare three methods used to combine multiple classifiers. Bayesian networks perform classification based on conditional probability. It is ineffective and easy to interpret as it assumes that the predictors are independent. Tree augmented naïve Bayes (TAN constructs a maximum weighted spanning tree that maximizes the likelihood of the training data, to perform classification.This tree structure eliminates the independent attribute assumption of naïve Bayesian networks. Behavior-knowledge space method works in two phases and can provide very good performances if large and representative data sets are available.
Bayesian Tracking of Visual Objects
Zheng, Nanning; Xue, Jianru
Tracking objects in image sequences involves performing motion analysis at the object level, which is becoming an increasingly important technology in a wide range of computer video applications, including video teleconferencing, security and surveillance, video segmentation, and editing. In this chapter, we focus on sequential Bayesian estimation techniques for visual tracking. We first introduce the sequential Bayesian estimation framework, which acts as the theoretic basis for visual tracking. Then, we present approaches to constructing representation models for specific objects.
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification......, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended...
Bayesian Inference: with ecological applications
Link, William A.; Barker, Richard J.
2010-01-01
This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.
A Focused Bayesian Information Criterion
Georges Nguefack-Tsague; Ingo Bulla
2014-01-01
Myriads of model selection criteria (Bayesian and frequentist) have been proposed in the literature aiming at selecting a single model regardless of its intended use. An honorable exception in the frequentist perspective is the “focused information criterion” (FIC) aiming at selecting a model based on the parameter of interest (focus). This paper takes the same view in the Bayesian context; that is, a model may be good for one estimand but bad for another. The proposed method exploits the Bay...
Bayesian analysis of CCDM Models
Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.
2016-01-01
Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, leads to negative creation pressure, which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical tools, at light of SN Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These approaches allow to compare models considering goodness of fit and numbe...
Bayesian Methods for Statistical Analysis
Puza, Borek
2015-01-01
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete c...
Hu, Jiang; Bai, ZhiDong
2016-12-01
In this paper, we will introduce the so called naive tests and give a brief review on the newly development. Naive testing methods are easy to understand and performs robust especially when the dimension is large. In this paper, we mainly focus on reviewing some naive testing methods for the mean vectors and covariance matrices of high dimensional populations and believe this naive test idea can be wildly used in many other testing problems.
Application of an efficient Bayesian discretization method to biomedical data
Directory of Open Access Journals (Sweden)
Gopalakrishnan Vanathi
2011-07-01
Full Text Available Abstract Background Several 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. Results On 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. Conclusions On 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.
Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach
Directory of Open Access Journals (Sweden)
Dirk Eilander
2014-01-01
Full Text Available Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir.
Bayesian Interpolation and Deconvolution
1992-07-01
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Dynamic Batch Bayesian Optimization
Azimi, Javad; Fern, Xiaoli
2011-01-01
Bayesian optimization (BO) algorithms try to optimize an unknown function that is expensive to evaluate using minimum number of evaluations/experiments. Most of the proposed algorithms in BO are sequential, where only one experiment is selected at each iteration. This method can be time inefficient when each experiment takes a long time and more than one experiment can be ran concurrently. On the other hand, requesting a fix-sized batch of experiments at each iteration causes performance inefficiency in BO compared to the sequential policies. In this paper, we present an algorithm that asks a batch of experiments at each time step t where the batch size p_t is dynamically determined in each step. Our algorithm is based on the observation that the sequence of experiments selected by the sequential policy can sometimes be almost independent from each other. Our algorithm identifies such scenarios and request those experiments at the same time without degrading the performance. We evaluate our proposed method us...
Abayomi, Kobi; Pizarro, Gonzalo
2013-01-01
We offer a straightforward framework for measurement of progress, across many dimensions, using cross-national social indices, which we classify as linear combinations of multivariate country level data onto a univariate score. We suggest a Bayesian approach which yields probabilistic (confidence type) intervals for the point estimates of country…
Abayomi, Kobi; Pizarro, Gonzalo
2013-01-01
We offer a straightforward framework for measurement of progress, across many dimensions, using cross-national social indices, which we classify as linear combinations of multivariate country level data onto a univariate score. We suggest a Bayesian approach which yields probabilistic (confidence type) intervals for the point estimates of country…
An Improved Bayesian with Application to Anti-Spam Email
Institute of Scientific and Technical Information of China (English)
ZHAN Chuan; LU Xian-liang; ZHOU Xu; HOU Meng-shu
2005-01-01
Along with the wide application of e-mail nowadays, many spam e-mails flood into people's email-boxes and cause catastrophes to their study and life. In anti-spam e-mails campaign, we depend on not only legal measures but also technological approaches. The Bayesian classifier provides a simple and effective approach to discriminate classification. This paper presents a new improved Bayesian-based anti-spam e-mail filter. We adopt a way of attribute selection based on word entropy, use vector weights which are represented by word frequency, and deduce its corresponding formula. It is proved that our filter improves total performances apparently in our experiment.
Bayesian Model Selection and Statistical Modeling
Ando, Tomohiro
2010-01-01
Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The quality of these solutions usually depends on the goodness of the constructed Bayesian model. Realizing how crucial this issue is, many researchers and practitioners have been extensively investigating the Bayesian model selection problem. This book provides comprehensive explanations of the concepts and derivations of the Bayesian approach for model selection and related criteria, including the Bayes factor, the Bayesian information criterion (BIC), the generalized BIC, and the pseudo marginal lik
Local Naive Bayes Nearest Neighbor for Image Classification
McCann, Sancho
2011-01-01
We present Local Naive Bayes Nearest Neighbor, an improvement to the NBNN image classification algorithm that increases classification accuracy and improves its ability to scale to large numbers of object classes. The key observation is that only the classes represented in the local neighborhood of a descriptor contribute significantly and reliably to their posterior probability estimates. Instead of maintaining a separate search structure for each class, we merge all of the reference data together into one search structure, allowing quick identification of a descriptor's local neighborhood. We show an increase in classification accuracy when we ignore adjustments to the more distant classes and show that the run time grows with the log of the number of classes rather than linearly in the number of classes as did the original. This gives a 100 times speed-up over the original method on the Caltech 256 dataset. We also provide the first head-to-head comparison of NBNN against spatial pyramid methods using a co...
Naive Dimensional Analysis for Three-Body Forces Without Pions
Griesshammer, H W
2005-01-01
For systems of three identical particles in which short-range forces produce shallow two-particle bound states, and in particular for the ``pion-less'' Effective Field Theory of Nuclear Physics, I extend and systematise the power-counting of three-body forces to all partial-waves and orders, including external currents. With low-energy observables independent of the details of short-distance dynamics, the typical strength of a three-body force is determined from the superficial degree of divergence of the three-body diagrams which contain only two-body forces. This na\\"ive dimensional analysis must be amended as the asymptotic solution to the leading-order Faddeev equation depends for large off-shell momenta crucially on the partial wave and spin-combination of the system. It is shown by analytic construction to be weaker in most channels with angular momentum smaller than 3 than expected. This demotes many three-nucleon forces to high orders. Observables like the quartet-S-scattering length are less sensitiv...
Similar Words Identification Using Naive and TF-IDF Method
Directory of Open Access Journals (Sweden)
Divya K.S.
2014-10-01
Full Text Available Requirement satisfaction is one of the most important factors to success of software. All the requirements that are specified by the customer should be satisfied in every phase of the development of the software. Satisfaction assessment is the determination of whether each component of the requirement has been addressed in the design document. The objective of this paper is to implement two methods to identify the satisfied requirements in the design document. To identify the satisfied requirements, similar words in both of the documents are determined. The methods such as Naive satisfaction assessment and TF-IDF satisfaction assessment are performed to determine the similar words that are present in the requirements document and design documents. The two methods are evaluated on the basis of the precision and recall value. To perform the stemming, the Porter’s stemming algorithm is used. The satisfaction assessment methods would determine the similarity in the requirement and design documents. The final result would give a accurate picture of the requirement satisfaction so that the defects can be determined at the early stage of software development. Since the defects determines at the early stage, the cost would be low to correct the defects.
Customer Credit Scoring Models on Bayesian Network Classification%贝叶斯网络个人信用评估模型
Institute of Scientific and Technical Information of China (English)
郭春香; 李旭升
2009-01-01
研究了朴素贝叶斯分类器、树增强朴素贝叶斯分类器2种贝叶斯网络信用评估模型的精度,用10层交叉验证在2个真实数据集上对贝叶斯网络信用评分模型进行了测试并与神经网络模型进行了比较.结果表明,贝叶斯网络信用评估模型具有较高的分类精度,在信用评估中具有优势.%This paper investigates the credit scoring accuracy of two Bayesian network models: naive Bayesian and tree augmented naive Bayesian. They are tested using 10-fold cross validation with two real world data sets, and compared with neural network models. Results demonstrate that the Bayesian network credit scoring models are competitive with neural network models and predominant in credit scoring domain.
Using Bayesian Learning to Classify College Algebra Students by Understanding in Real-Time
Cousino, Andrew
2013-01-01
The goal of this work is to provide instructors with detailed information about their classes at each assignment during the term. The information is both on an individual level and at the aggregate level. We used the large number of grades, which are available online these days, along with data-mining techniques to build our models. This enabled…
Development of a Bayesian Classifier for Breast Cancer Risk Stratification: A Feasibility Study
2010-03-29
preceding 3 months, had breast fine needle aspiration within the preceding 1 month, were pregnant, had electrically powered implanted devices (eg, pacemaker...EIS), breast imaging, and biopsy data) from a prospective pilot screening trial in younger women (N = 591). Receiver operating characteristic curve...a cross-validation exercise could provide in terms of risk stratification in a larger population. Results: Independent predictors of biopsy outcome
Bayesian seismic AVO inversion
Energy Technology Data Exchange (ETDEWEB)
Buland, Arild
2002-07-01
A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P-wave velocity, S-wave velocity and density. Distributions for other elastic parameters can also be assessed, for example acoustic impedance, shear impedance and P-wave to S-wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance, hence exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3-D dataset from the Sleipner Field. The results show good agreement with well logs but the uncertainty is high. The stochastic model includes uncertainties of both the elastic parameters, the wavelet and the seismic and well log data. The posterior distribution is explored by Markov chain Monte Carlo simulation using the Gibbs sampler algorithm. The inversion algorithm has been tested on a seismic line from the Heidrun Field with two wells located on the line. The uncertainty of the estimated wavelet is low. In the Heidrun examples the effect of including uncertainty of the wavelet and the noise level was marginal with respect to the AVO inversion results. We have developed a 3-D linearized AVO inversion method with spatially coupled model parameters where the objective is to obtain posterior distributions for P-wave velocity, S
Bayesian microsaccade detection
Mihali, Andra; van Opheusden, Bas; Ma, Wei Ji
2017-01-01
Microsaccades are high-velocity fixational eye movements, with special roles in perception and cognition. The default microsaccade detection method is to determine when the smoothed eye velocity exceeds a threshold. We have developed a new method, Bayesian microsaccade detection (BMD), which performs inference based on a simple statistical model of eye positions. In this model, a hidden state variable changes between drift and microsaccade states at random times. The eye position is a biased random walk with different velocity distributions for each state. BMD generates samples from the posterior probability distribution over the eye state time series given the eye position time series. Applied to simulated data, BMD recovers the “true” microsaccades with fewer errors than alternative algorithms, especially at high noise. Applied to EyeLink eye tracker data, BMD detects almost all the microsaccades detected by the default method, but also apparent microsaccades embedded in high noise—although these can also be interpreted as false positives. Next we apply the algorithms to data collected with a Dual Purkinje Image eye tracker, whose higher precision justifies defining the inferred microsaccades as ground truth. When we add artificial measurement noise, the inferences of all algorithms degrade; however, at noise levels comparable to EyeLink data, BMD recovers the “true” microsaccades with 54% fewer errors than the default algorithm. Though unsuitable for online detection, BMD has other advantages: It returns probabilities rather than binary judgments, and it can be straightforwardly adapted as the generative model is refined. We make our algorithm available as a software package. PMID:28114483
A Focused Bayesian Information Criterion
Directory of Open Access Journals (Sweden)
Georges Nguefack-Tsague
2014-01-01
Full Text Available Myriads of model selection criteria (Bayesian and frequentist have been proposed in the literature aiming at selecting a single model regardless of its intended use. An honorable exception in the frequentist perspective is the “focused information criterion” (FIC aiming at selecting a model based on the parameter of interest (focus. This paper takes the same view in the Bayesian context; that is, a model may be good for one estimand but bad for another. The proposed method exploits the Bayesian model averaging (BMA machinery to obtain a new criterion, the focused Bayesian model averaging (FoBMA, for which the best model is the one whose estimate is closest to the BMA estimate. In particular, for two models, this criterion reduces to the classical Bayesian model selection scheme of choosing the model with the highest posterior probability. The new method is applied in linear regression, logistic regression, and survival analysis. This criterion is specially important in epidemiological studies in which the objective is often to determine a risk factor (focus for a disease, adjusting for potential confounding factors.
Fujino, Akinori; Ueda, Naonori; Saito, Kazumi
2008-03-01
This paper presents a method for designing semi-supervised classifiers trained on labeled and unlabeled samples. We focus on probabilistic semi-supervised classifier design for multi-class and single-labeled classification problems, and propose a hybrid approach that takes advantage of generative and discriminative approaches. In our approach, we first consider a generative model trained by using labeled samples and introduce a bias correction model, where these models belong to the same model family, but have different parameters. Then, we construct a hybrid classifier by combining these models based on the maximum entropy principle. To enable us to apply our hybrid approach to text classification problems, we employed naive Bayes models as the generative and bias correction models. Our experimental results for four text data sets confirmed that the generalization ability of our hybrid classifier was much improved by using a large number of unlabeled samples for training when there were too few labeled samples to obtain good performance. We also confirmed that our hybrid approach significantly outperformed generative and discriminative approaches when the performance of the generative and discriminative approaches was comparable. Moreover, we examined the performance of our hybrid classifier when the labeled and unlabeled data distributions were different.
PERFORMANCE EVALUATION OF VARIOUS STATISTICAL CLASSIFIERS IN DETECTING THE DISEASED CITRUS LEAVES
Directory of Open Access Journals (Sweden)
SUDHEER REDDY BANDI
2013-02-01
Full Text Available Citrus fruits are in lofty obligation because the humans consume them daily. This research aims to amend citrus production, which knows a low upshot bourgeois on the production and complex during measurements. Nowadays citrus plants grappling some traits/diseases. Harm of the insect is one of the major trait/disease. Insecticides are not ever evidenced effectual because insecticides may be toxic to some gracious of birds. Farmers get outstanding difficulties in detecting the diseases ended open eye and also it is quite expensive.Machine vision and Image processing techniques helps in sleuthing the disease mark in citrus leaves and sound job. In this search, Citrus leaves of four classes like Normal, Greasy spot, Melanose and Scab are collected and investigated using texture analysis based on the Color Co-occurrence Method (CCM to take Hue, Saturation and Intensity (HSI features. In the arrangement form, the features are categorised for all leafage conditions using k-Nearest Neighbor (kNN, Naive Bayes classifier (NBC, Linear Discriminate Analysis (LDA classifier and Random Forest Tree Algorithm classifier (RFT. The experimental results inform that proposed attack significantly supports 98.75% quality in automated detection of regular and struck leaves using texture psychotherapy based CCM method using LDA formula. Eventually all the classifiers are compared using Earphone Operative Characteristic contour and analyzed the performance of all the classifiers.
Hybrid k -Nearest Neighbor Classifier.
Yu, Zhiwen; Chen, Hantao; Liuxs, Jiming; You, Jane; Leung, Hareton; Han, Guoqiang
2016-06-01
Conventional k -nearest neighbor (KNN) classification approaches have several limitations when dealing with some problems caused by the special datasets, such as the sparse problem, the imbalance problem, and the noise problem. In this paper, we first perform a brief survey on the recent progress of the KNN classification approaches. Then, the hybrid KNN (HBKNN) classification approach, which takes into account the local and global information of the query sample, is designed to address the problems raised from the special datasets. In the following, the random subspace ensemble framework based on HBKNN (RS-HBKNN) classifier is proposed to perform classification on the datasets with noisy attributes in the high-dimensional space. Finally, the nonparametric tests are proposed to be adopted to compare the proposed method with other classification approaches over multiple datasets. The experiments on the real-world datasets from the Knowledge Extraction based on Evolutionary Learning dataset repository demonstrate that RS-HBKNN works well on real datasets, and outperforms most of the state-of-the-art classification approaches.
Directory of Open Access Journals (Sweden)
Datta Susmita
2010-08-01
Full Text Available Abstract Background Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal in any given classification application. In addition, for most classification problems, selecting the best performing classification algorithm amongst a number of competing algorithms is a difficult task for various reasons. As for example, the order of performance may depend on the performance measure employed for such a comparison. In this work, we present a novel adaptive ensemble classifier constructed by combining bagging and rank aggregation that is capable of adaptively changing its performance depending on the type of data that is being classified. The attractive feature of the proposed classifier is its multi-objective nature where the classification results can be simultaneously optimized with respect to several performance measures, for example, accuracy, sensitivity and specificity. We also show that our somewhat complex strategy has better predictive performance as judged on test samples than a more naive approach that attempts to directly identify the optimal classifier based on the training data performances of the individual classifiers. Results We illustrate the proposed method with two simulated and two real-data examples. In all cases, the ensemble classifier performs at the level of the best individual classifier comprising the ensemble or better. Conclusions For complex high-dimensional datasets resulting from present day high-throughput experiments, it may be wise to consider a number of classification algorithms combined with dimension reduction techniques rather than a fixed standard algorithm set a priori.
Bayesian Methods and Universal Darwinism
Campbell, John
2010-01-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a 'copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that system...
Probability biases as Bayesian inference
Directory of Open Access Journals (Sweden)
Andre; C. R. Martins
2006-11-01
Full Text Available In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions. %XX In that sense, it should be no surprise that humans reason with % probability as it has been observed.
Introduction to Bayesian modelling in dental research.
Gilthorpe, M S; Maddick, I H; Petrie, A
2000-12-01
To explain the concepts and application of Bayesian modelling and how it can be applied to the analysis of dental research data. Methodological in nature, this article introduces Bayesian modelling through hypothetical dental examples. The synthesis of RCT results with previous evidence, including expert opinion, is used to illustrate full Bayesian modelling. Meta-analysis, in the form of empirical Bayesian modelling, is introduced. An example of full Bayesian modelling is described for the synthesis of evidence from several studies that investigate the success of root canal treatment. Hierarchical (Bayesian) modelling is demonstrated for a survey of childhood caries, where surface data is nested within subjects. Bayesian methods enhance interpretation of research evidence through the synthesis of information from multiple sources. Bayesian modelling is now readily accessible to clinical researchers and is able to augment the application of clinical decision making in the development of guidelines and clinical practice.
Bayesian modeling using WinBUGS
Ntzoufras, Ioannis
2009-01-01
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...
75 FR 707 - Classified National Security Information
2010-01-05
... National Security Information Memorandum of December 29, 2009--Implementation of the Executive Order ``Classified National Security Information'' Order of December 29, 2009--Original Classification Authority #0... 13526 of December 29, 2009 Classified National Security Information This order prescribes a...
Classifier Assignment by Corpus-based Approach
Sornlertlamvanich, V; Meknavin, S; Sornlertlamvanich, Virach; Pantachat, Wantanee; Meknavin, Surapant
1994-01-01
This paper presents an algorithm for selecting an appropriate classifier word for a noun. In Thai language, it frequently happens that there is fluctuation in the choice of classifier for a given concrete noun, both from the point of view of the whole spe ech community and individual speakers. Basically, there is no exect rule for classifier selection. As far as we can do in the rule-based approach is to give a default rule to pick up a corresponding classifier of each noun. Registration of classifier for each noun is limited to the type of unit classifier because other types are open due to the meaning of representation. We propose a corpus-based method (Biber, 1993; Nagao, 1993; Smadja, 1993) which generates Noun Classifier Associations (NCA) to overcome the problems in classifier assignment and semantic construction of noun phrase. The NCA is created statistically from a large corpus and recomposed under concept hierarchy constraints and frequency of occurrences.
2011-01-01
Our purpose was to characterize the risks of osteoporosis-related subtrochanteric fractures in bisphosphonate-naive individuals. Baseline characteristics of patients enrolled in the HORIZON-Recurrent Fracture Trial with a study-qualifying hip fracture were examined, comparing those who sustained incident subtrochanteric fractures with those sustaining other hip fractures. Subjects were bisphosphonate-naive or had a bisphosphonate washout period of 6–24 months and subsequently received an annu...
Aggregation Operator Based Fuzzy Pattern Classifier Design
DEFF Research Database (Denmark)
Mönks, Uwe; Larsen, Henrik Legind
2009-01-01
This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable. The perfor....... The performances of novel classifiers using substitutes of MFPC's geometric mean aggregator are benchmarked in the scope of an image processing application against the MFPC to reveal classification improvement potentials for obtaining higher classification rates....
Bayesian Missile System Reliability from Point Estimates
2014-10-28
OCT 2014 2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Bayesian Missile System Reliability from Point Estimates 5a. CONTRACT...Principle (MEP) to convert point estimates to probability distributions to be used as priors for Bayesian reliability analysis of missile data, and...illustrate this approach by applying the priors to a Bayesian reliability model of a missile system. 15. SUBJECT TERMS priors, Bayesian , missile
Attention in a bayesian framework
DEFF Research Database (Denmark)
Whiteley, Louise Emma; Sahani, Maneesh
2012-01-01
, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental......The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models...
Bayesian test and Kuhn's paradigm
Institute of Scientific and Technical Information of China (English)
Chen Xiaoping
2006-01-01
Kuhn's theory of paradigm reveals a pattern of scientific progress,in which normal science alternates with scientific revolution.But Kuhn underrated too much the function of scientific test in his pattern,because he focuses all his attention on the hypothetico-deductive schema instead of Bayesian schema.This paper employs Bayesian schema to re-examine Kuhn's theory of paradigm,to uncover its logical and rational components,and to illustrate the tensional structure of logic and belief,rationality and irrationality,in the process of scientific revolution.
Perception, illusions and Bayesian inference.
Nour, Matthew M; Nour, Joseph M
2015-01-01
Descriptive psychopathology makes a distinction between veridical perception and illusory perception. In both cases a perception is tied to a sensory stimulus, but in illusions the perception is of a false object. This article re-examines this distinction in light of new work in theoretical and computational neurobiology, which views all perception as a form of Bayesian statistical inference that combines sensory signals with prior expectations. Bayesian perceptual inference can solve the 'inverse optics' problem of veridical perception and provides a biologically plausible account of a number of illusory phenomena, suggesting that veridical and illusory perceptions are generated by precisely the same inferential mechanisms.
Human Naive Pluripotent Stem Cells Model X Chromosome Dampening and X Inactivation.
Sahakyan, Anna; Kim, Rachel; Chronis, Constantinos; Sabri, Shan; Bonora, Giancarlo; Theunissen, Thorold W; Kuoy, Edward; Langerman, Justin; Clark, Amander T; Jaenisch, Rudolf; Plath, Kathrin
2017-01-05
Naive human embryonic stem cells (hESCs) can be derived from primed hESCs or directly from blastocysts, but their X chromosome state has remained unresolved. Here, we show that the inactive X chromosome (Xi) of primed hESCs was reactivated in naive culture conditions. Like cells of the blastocyst, the resulting naive cells contained two active X chromosomes with XIST expression and chromosome-wide transcriptional dampening and initiated XIST-mediated X inactivation upon differentiation. Both establishment of and exit from the naive state (differentiation) happened via an XIST-negative XaXa intermediate. Together, these findings identify a cell culture system for functionally exploring the two X chromosome dosage compensation processes in early human development: X dampening and X inactivation. However, remaining differences between naive hESCs and embryonic cells related to mono-allelic XIST expression and non-random X inactivation highlight the need for further culture improvement. As the naive state resets Xi abnormalities seen in primed hESCs, it may provide cells better suited for downstream applications.
Naive Juveniles Are More Likely to Become Breeders after Witnessing Predator Mobbing.
Griesser, Michael; Suzuki, Toshitaka N
2017-01-01
Responding appropriately during the first predatory attack in life is often critical for survival. In many social species, naive juveniles acquire this skill from conspecifics, but its fitness consequences remain virtually unknown. Here we experimentally demonstrate how naive juvenile Siberian jays (Perisoreus infaustus) derive a long-term fitness benefit from witnessing knowledgeable adults mobbing their principal predator, the goshawk (Accipiter gentilis). Siberian jays live in family groups of two to six individuals that also can include unrelated nonbreeders. Field observations showed that Siberian jays encounter predators only rarely, and, indeed, naive juveniles do not respond to predator models when on their own but do when observing other individuals mobbing them. Predator exposure experiments demonstrated that naive juveniles had a substantially higher first-winter survival after observing knowledgeable group members mobbing a goshawk model, increasing their likelihood of acquiring a breeding position later in life. Previous research showed that naive individuals may learn from others how to respond to predators, care for offspring, or choose mates, generally assuming that social learning has long-term fitness consequences without empirical evidence. Our results demonstrate a long-term fitness benefit of vertical social learning for naive individuals in the wild, emphasizing its evolutionary importance in animals, including humans.
15 CFR 4.8 - Classified Information.
2010-01-01
... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Classified Information. 4.8 Section 4... INFORMATION Freedom of Information Act § 4.8 Classified Information. In processing a request for information..., the information shall be reviewed to determine whether it should remain classified. Ordinarily...
Plug & Play object oriented Bayesian networks
DEFF Research Database (Denmark)
Bangsø, Olav; Flores, J.; Jensen, Finn Verner
2003-01-01
Object oriented Bayesian networks have proven themselves useful in recent years. The idea of applying an object oriented approach to Bayesian networks has extended their scope to larger domains that can be divided into autonomous but interrelated entities. Object oriented Bayesian networks have b...
A Bayesian Nonparametric Approach to Test Equating
Karabatsos, George; Walker, Stephen G.
2009-01-01
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
Bayesian Model Averaging for Propensity Score Analysis
Kaplan, David; Chen, Jianshen
2013-01-01
The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…
Bayesian networks and food security - An introduction
Stein, A.
2004-01-01
This paper gives an introduction to Bayesian networks. Networks are defined and put into a Bayesian context. Directed acyclical graphs play a crucial role here. Two simple examples from food security are addressed. Possible uses of Bayesian networks for implementation and further use in decision sup
Learning to Detect Spam: Naive-Euclidean Approach
Directory of Open Access Journals (Sweden)
Tony Y.T. Chan
2008-12-01
Full Text Available A method is proposed for learning to classify spam and nonspamemails. It combines the strategy of the Best Stepwise Feature Selection with a classifier of Euclidean nearest-neighbor. Each text email is first transformed into a vector of D-dimensional Euclidean space. Emails were divided into training and test sets in the manner of 10-fold crossvalidation. Three experiments were performed, and their elapsed CPU times and accuracies reported. The proposed spam detection learner was found to be extremely fast in recognition and with good error rates. It could be used as a baseline learning agent, in terms of CPU time and accuracy, against which other learning agents can be measured.
Data characteristics that determine classifier performance
CSIR Research Space (South Africa)
Van der Walt, Christiaan M
2006-11-01
Full Text Available classifiers. 10-fold cross-validation is used to evaluate and compare the performance of the classifiers on the different data sets. 3.1. Artificial data generation Multivariate Gaussian distributions are used to generate artificial data sets. We use d...NN) classifier [8], the multi- layer perceptron (MLP) and support vector machines (SVMs) [9]. The NB, DT, kNN, MLP and SVM classifiers are all implementations of the machine learning package Weka [10]. The Gaussian classifier is a Matlab implementation...
Bayesian Statistics at Work: the Troublesome Extraction of the CKM Phase {alpha}
Energy Technology Data Exchange (ETDEWEB)
Charles, J. [CPT, Luminy Case 907, F-13288 Marseille Cedex 9 (France); Hoecker, A. [CERN, CH-1211 Geneva 23 (Switzerland); Lacker, H. [TU Dresden, IKTP, D-01062 Dresden (Germany); Le Diberder, F.R. [LAL, CNRS/IN2P3, Universite Paris-Sud 11, Bat. 200, BP 34, F-91898 Orsay Cedex (France); T' Jampens, S. [LAPP, CNRS/IN2P3, Universite de Savoie, 9 Chemin de Bellevue, BP 110, F-74941 Annecy-le-Vieux Cedex (France)
2007-04-15
In Bayesian statistics, one's prior beliefs about underlying model parameters are revised with the information content of observed data from which, using Bayes' rule, a posterior belief is obtained. A non-trivial example taken from the isospin analysis of B {yields} PP (P = {pi} or {rho}) decays in heavy-flavor physics is chosen to illustrate the effect of the naive 'objective' choice of flat priors in a multi- dimensional parameter space in presence of mirror solutions. It is demonstrated that the posterior distribution for the parameter of interest, the phase {alpha}, strongly depends on the choice of the parameterization in which the priors are uniform, and on the validity range in which the (un-normalizable) priors are truncated. We prove that the most probable values found by the Bayesian treatment do not coincide with the explicit analytical solutions, in contrast to the frequentist approach. It is also shown in the appendix that the {alpha} {yields} 0 limit cannot be consistently treated in the Bayesian paradigm, because the latter violates the physical symmetries of the problem. (authors)
Bayesian Classification of Image Structures
DEFF Research Database (Denmark)
Goswami, Dibyendu; Kalkan, Sinan; Krüger, Norbert
2009-01-01
In this paper, we describe work on Bayesian classi ers for distinguishing between homogeneous structures, textures, edges and junctions. We build semi-local classiers from hand-labeled images to distinguish between these four different kinds of structures based on the concept of intrinsic dimensi...
Bayesian Analysis of Experimental Data
Directory of Open Access Journals (Sweden)
Lalmohan Bhar
2013-10-01
Full Text Available Analysis of experimental data from Bayesian point of view has been considered. Appropriate methodology has been developed for application into designed experiments. Normal-Gamma distribution has been considered for prior distribution. Developed methodology has been applied to real experimental data taken from long term fertilizer experiments.
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new...
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis
configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed...
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis Linda
2006-01-01
configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for the salt and pepper noise. The inference in the model is discussed...
Differentiated Bayesian Conjoint Choice Designs
Z. Sándor (Zsolt); M. Wedel (Michel)
2003-01-01
textabstractPrevious conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
2013-01-01
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...
Bayesian Evidence and Model Selection
Knuth, Kevin H; Malakar, Nabin K; Mubeen, Asim M; Placek, Ben
2014-01-01
In this paper we review the concept of the Bayesian evidence and its application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Application to several practical examples within the context of signal processing are discussed.
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...
Bayesian stable isotope mixing models
In this paper we review recent advances in Stable Isotope Mixing Models (SIMMs) and place them into an over-arching Bayesian statistical framework which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixtur...
Bayesian tests of measurement invariance
Verhagen, A.J.; Fox, J.P.
2013-01-01
Random item effects models provide a natural framework for the exploration of violations of measurement invariance without the need for anchor items. Within the random item effects modelling framework, Bayesian tests (Bayes factor, deviance information criterion) are proposed which enable multiple m
Bayesian NL interpretation and learning
Zeevat, H.
2011-01-01
Everyday natural language communication is normally successful, even though contemporary computational linguistics has shown that NL is characterised by very high degree of ambiguity and the results of stochastic methods are not good enough to explain the high success rate. Bayesian natural language
Bayesian analysis of binary sequences
Torney, David C.
2005-03-01
This manuscript details Bayesian methodology for "learning by example", with binary n-sequences encoding the objects under consideration. Priors prove influential; conformable priors are described. Laplace approximation of Bayes integrals yields posterior likelihoods for all n-sequences. This involves the optimization of a definite function over a convex domain--efficiently effectuated by the sequential application of the quadratic program.
Bayesian Networks and Influence Diagrams
DEFF Research Database (Denmark)
Kjærulff, Uffe Bro; Madsen, Anders Læsø
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new...
Image Classifying Registration and Dynamic Region Merging
Directory of Open Access Journals (Sweden)
Himadri Nath Moulick
2013-07-01
Full Text Available In this paper, we address a complex image registration issue arising when the dependencies between intensities of images to be registered are not spatially homogeneous. Such a situation is frequentlyencountered in medical imaging when a pathology present in one of the images modifies locally intensity dependencies observed on normal tissues. Usual image registration models, which are based on a single global intensity similarity criterion, fail to register such images, as they are blind to local deviations of intensity dependencies. Such a limitation is also encountered in contrast enhanced images where there exist multiple pixel classes having different properties of contrast agent absorption. In this paper, we propose a new model in which the similarity criterion is adapted locally to images by classification of image intensity dependencies. Defined in a Bayesian framework, the similarity criterion is a mixture of probability distributions describing dependencies on two classes. The model also includes a class map which locates pixels of the two classes and weights the two mixture components. The registration problem is formulated both as an energy minimization problem and as a Maximum A Posteriori (MAP estimation problem. It is solved using a gradient descent algorithm. In the problem formulation and resolution, the image deformation and the class map are estimated at the same time, leading to an original combination of registration and classification that we call image classifying registration. Whenever sufficient information about class location is available in applications, the registration can also be performed on its own by fixing a given class map. Finally, we illustrate the interest of our model on two real applications from medical imaging: template-based segmentation of contrast-enhanced images and lesion detection in mammograms. We also conduct an evaluation of our model on simulated medical data and show its ability to take into
Bayesian Alternation During Tactile Augmentation
Directory of Open Access Journals (Sweden)
Caspar Mathias Goeke
2016-10-01
Full Text Available A large number of studies suggest that the integration of multisensory signals by humans is well described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition, rotation only (native condition, and both augmented and native information (bimodal condition. Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants’ responses with a probit model and calculated the just notable difference (JND. Then we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χred2 = 1.67 than the Bayesian integration model (χred2= 4.34. Slightly higher accuracy showed a non-Bayesian winner takes all model (χred2= 1.64, which either used only native or only augmented values per subject for prediction. However the performance of the Bayesian alternation model could be substantially improved (χred2= 1.09 utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in
Urgesi, R; Pelecca, G; Cianci, R; Masini, A; Zampaletta, C; Riccioni, ME; Faggiani, R
2011-01-01
BACKGROUND: Clarithromycin resistance has decreased the eradication rates of Helicobacter pylori. AIMS: To determine whether a 10-day course of sequential therapy (ST) is more effective at eradicating H pylori infection than triple therapy (TT) in the first or second line, and to assess side effects and compliance with therapy. METHODS: One hundred sixty treatment-naive and 40 non-treatment-naive patients who were positive for H pylori infection by 13C-urea breath test or endoscopy were enrolled. Eighty of 160 patients underwent TT, while 80 of 160 underwent ST with omeprazole (20 mg) plus amoxicillin (1 g) twice/day for five days, followed by omeprazole (20 mg) with tinidazole (500 mg) twice/day and clarithromycin (500 mg) twice/day for five consecutive days. H pylori eradication was evaluated by 13C-urea breath test no sooner than four weeks after the end of treatment. RESULTS: Eradication was achieved in 59 of 80 treatment-naive patients treated with TT (74%), in 74 of 80 patients treated with ST (93%), and in 38 of 40 non-treatment-naive patients (95%). Eradication rates in treatment-naive patients with ST were statistically significantly higher than TT (92.5% versus 73.7%; P=0.0015; OR 4.39 [95% CI 1.66 to 11.58]). Mild adverse effects were reported for both regimens. CONCLUSIONS: ST appears to be a well-tolerated, promising therapy; however, randomized controlled trials with larger and more diverse sample populations are needed before it can be recommended as a first-line treatment. PMID:21766091
Bayesian analysis of rare events
Energy Technology Data Exchange (ETDEWEB)
Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang
2016-06-01
In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.
Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.
Zhang, Yi; Ren, Jinchang; Jiang, Jianmin
2015-01-01
Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.
Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations
Directory of Open Access Journals (Sweden)
Yi Zhang
2015-01-01
Full Text Available Maximum likelihood classifier (MLC and support vector machines (SVM are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.
Koch, Tobias; Schultze, Martin; Jeon, Minjeong; Nussbeck, Fridtjof W; Praetorius, Anna-Katharina; Eid, Michael
2016-01-01
Multirater (multimethod, multisource) studies are increasingly applied in psychology. Eid and colleagues (2008) proposed a multilevel confirmatory factor model for multitrait-multimethod (MTMM) data combining structurally different and multiple independent interchangeable methods (raters). In many studies, however, different interchangeable raters (e.g., peers, subordinates) are asked to rate different targets (students, supervisors), leading to violations of the independence assumption and to cross-classified data structures. In the present work, we extend the ML-CFA-MTMM model by Eid and colleagues (2008) to cross-classified multirater designs. The new C4 model (Cross-Classified CTC[M-1] Combination of Methods) accounts for nonindependent interchangeable raters and enables researchers to explicitly model the interaction between targets and raters as a latent variable. Using a real data application, it is shown how credibility intervals of model parameters and different variance components can be obtained using Bayesian estimation techniques.
Differential T cell receptor-mediated signaling in naive and memory CD4 T cells.
Farber, D L; Acuto, O; Bottomly, K
1997-08-01
Naive and memory CD4 T cells differ in cell surface phenotype, function, activation requirements, and modes of regulation. To investigate the molecular bases for the dichotomies between naive and memory CD4 T cells and to understand how the T cell receptor (TCR) directs diverse functional outcomes, we investigated proximal signaling events triggered through the TCR/CD3 complex in naive and memory CD4 T cell subsets isolated on the basis of CD45 isoform expression. Naive CD4 T cells signal through TCR/CD3 similar to unseparated CD4 T cells, producing multiple tyrosine-phosphorylated protein species overall and phosphorylating the T cell-specific ZAP-70 tyrosine kinase which is recruited to the CD3zeta subunit of the TCR. Memory CD4 T cells, however, exhibit a unique pattern of signaling through TCR/CD3. Following stimulation through TCR/CD3, memory CD4 T cells produce fewer species of tyrosine-phosphorylated substrates and fail to phosphorylate ZAP-70, yet unphosphorylated ZAP-70 can associate with the TCR/CD3 complex. Moreover, a 26/28-kDa phosphorylated doublet is associated with CD3zeta in resting and activated memory but not in naive CD4 T cells. Despite these differences in the phosphorylation of ZAP-70 and CD3-associated proteins, the ZAP-70-related kinase, p72syk, exhibits similar phosphorylation in naive and memory T cell subsets, suggesting that this kinase could function in place of ZAP-70 in memory CD4 T cells. These results indicate that proximal signals are differentially coupled to the TCR in naive versus memory CD4 T cells, potentially leading to distinct downstream signaling events and ultimately to the diverse functions elicited by these two CD4 T cell subsets.
STATISTICAL BAYESIAN ANALYSIS OF EXPERIMENTAL DATA.
Directory of Open Access Journals (Sweden)
AHLAM LABDAOUI
2012-12-01
Full Text Available The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computational tools used in modern Bayesian econometrics. Some of the most important methods of posterior simulation are Monte Carlo integration, importance sampling, Gibbs sampling and the Metropolis- Hastings algorithm. The Bayesian should also be able to put the theory and computational tools together in the context of substantive empirical problems. We focus primarily on recent developments in Bayesian computation. Then we focus on particular models. Inevitably, we combine theory and computation in the context of particular models. Although we have tried to be reasonably complete in terms of covering the basic ideas of Bayesian theory and the computational tools most commonly used by the Bayesian, there is no way we can cover all the classes of models used in econometrics. We propose to the user of analysis of variance and linear regression model.
Bayesian methods for measures of agreement
Broemeling, Lyle D
2009-01-01
Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process.The author employs a Bayesian approach to provide statistical inferences based on various models of intra- and interrater agreement. He presents many examples that illustrate the Bayesian mode of reasoning and explains elements of a Bayesian application, including prior information, experimental information, the likelihood function, posterior distribution, and predictive distribution. The appendices provide the necessary theoretical foundation to understand Bayesian methods as well as introduce the fundamentals of programming and executing the WinBUGS software.Taking a Bayesian approach to inference, this hands-on book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation...
22 CFR 125.3 - Exports of classified technical data and classified defense articles.
2010-04-01
... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Exports of classified technical data and... IN ARMS REGULATIONS LICENSES FOR THE EXPORT OF TECHNICAL DATA AND CLASSIFIED DEFENSE ARTICLES § 125.3 Exports of classified technical data and classified defense articles. (a) A request for authority...
Pavement Crack Classifiers: A Comparative Study
Directory of Open Access Journals (Sweden)
S. Siddharth
2012-12-01
Full Text Available Non Destructive Testing (NDT is an analysis technique used to inspect metal sheets and components without harming the product. NDT do not cause any change after inspection; this technique saves money and time in product evaluation, research and troubleshooting. In this study the objective is to perform NDT using soft computing techniques. Digital images are taken; Gray Level Co-occurrence Matrix (GLCM extracts features from these images. Extracted features are then fed into the classifiers which classifies them into images with and without cracks. Three major classifiers: Neural networks, Support Vector Machine (SVM and Linear classifiers are taken for the classification purpose. Performances of these classifiers are assessed and the best classifier for the given data is chosen.
Bayesian object classification of gold nanoparticles
Konomi, Bledar A.
2013-06-01
The properties of materials synthesized with nanoparticles (NPs) are highly correlated to the sizes and shapes of the nanoparticles. The transmission electron microscopy (TEM) imaging technique can be used to measure the morphological characteristics of NPs, which can be simple circles or more complex irregular polygons with varying degrees of scales and sizes. A major difficulty in analyzing the TEM images is the overlapping of objects, having different morphological properties with no specific information about the number of objects present. Furthermore, the objects lying along the boundary render automated image analysis much more difficult. To overcome these challenges, we propose a Bayesian method based on the marked-point process representation of the objects. We derive models, both for the marks which parameterize the morphological aspects and the points which determine the location of the objects. The proposed model is an automatic image segmentation and classification procedure, which simultaneously detects the boundaries and classifies the NPs into one of the predetermined shape families. We execute the inference by sampling the posterior distribution using Markov chainMonte Carlo (MCMC) since the posterior is doubly intractable. We apply our novel method to several TEM imaging samples of gold NPs, producing the needed statistical characterization of their morphology. © Institute of Mathematical Statistics, 2013.
Pertussis toxin activates adult and neonatal naive human CD4+ T lymphocytes.
Tonon, Sandrine; Badran, Bassam; Benghiat, Fleur Samantha; Goriely, Stanislas; Flamand, Véronique; Willard-Gallo, Karen; Willems, Fabienne; Goldman, Michel; De Wit, Dominique
2006-07-01
Pertussis toxin (PTX) is known to be mitogenic for T lymphocytes, but its direct action on naive human T cells has not been specified. Herein, we show that PTX induces the proliferation of purified adult CD45RA(+)CD4(+) T cells independently of its ADP-ribosyltransferase activity. PTX directly induces TNF-alpha and IL-2 mRNA expression, modulates the level of several cell surface receptors and induces Forkhead box p3 (Foxp3) protein accumulation in naive CD4(+) T cells. Addition of autologous dendritic cells was found to be required for the production of high levels of IFN-gamma by PTX-stimulated naive T cells. These effects of PTX occurred in conjunction with activation of NF-kappaB and NFAT transcription factors. Overall, responses of neonatal CD4(+) T cells to PTX were similar to those of adult CD45RA(+)CD4(+) naive T cells except for their blunted CD40 ligand up-regulation. We suggest that the adjuvant properties of PTX during primary cell-mediated immune responses involve a direct action on naive T lymphocytes in addition to activation of antigen-presenting cells.
Bim/Bcl-2 balance is critical for maintaining naive and memory T cell homeostasis
Wojciechowski, Sara; Tripathi, Pulak; Bourdeau, Tristan; Acero, Luis; Grimes, H. Leighton; Katz, Jonathan D.; Finkelman, Fred D.; Hildeman, David A.
2007-01-01
We examined the role of the antiapoptotic molecule Bcl-2 in combating the proapoptotic molecule Bim in control of naive and memory T cell homeostasis using Bcl-2−/− mice that were additionally deficient in one or both alleles of Bim. Naive T cells were significantly decreased in Bim+/−Bcl-2−/− mice, but were largely restored in Bim−/−Bcl-2−/− mice. Similarly, a synthetic Bcl-2 inhibitor killed wild-type, but not Bim−/−, T cells. Further, T cells from Bim+/−Bcl-2−/− mice died rapidly ex vivo and were refractory to cytokine-driven survival in vitro. In vivo, naive CD8+ T cells required Bcl-2 to combat Bim to maintain peripheral survival, whereas naive CD4+ T cells did not. In contrast, Bim+/−Bcl-2−/− mice generated relatively normal numbers of memory T cells after lymphocytic choriomeningitis virus infection. Accumulation of memory T cells in Bim+/−Bcl-2−/− mice was likely caused by their increased proliferative renewal because of the lymphopenic environment of the mice. Collectively, these data demonstrate a critical role for a balance between Bim and Bcl-2 in controlling homeostasis of naive and memory T cells. PMID:17591857
Comparing different classifiers for automatic age estimation.
Lanitis, Andreas; Draganova, Chrisina; Christodoulou, Chris
2004-02-01
We describe a quantitative evaluation of the performance of different classifiers in the task of automatic age estimation. In this context, we generate a statistical model of facial appearance, which is subsequently used as the basis for obtaining a compact parametric description of face images. The aim of our work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image. For this application, we have tested different classifiers: a classifier based on the use of quadratic functions for modeling the relationship between face model parameters and age, a shortest distance classifier, and artificial neural network based classifiers. We also describe variations to the basic method where we use age-specific and/or appearance specific age estimation methods. In this context, we use age estimation classifiers for each age group and/or classifiers for different clusters of subjects within our training set. In those cases, part of the classification procedure is devoted to choosing the most appropriate classifier for the subject/age range in question, so that more accurate age estimates can be obtained. We also present comparative results concerning the performance of humans and computers in the task of age estimation. Our results indicate that machines can estimate the age of a person almost as reliably as humans.
Bayesian Inference for Radio Observations
Lochner, Michelle; Zwart, Jonathan T L; Smirnov, Oleg; Bassett, Bruce A; Oozeer, Nadeem; Kunz, Martin
2015-01-01
(Abridged) New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inaccurate uncertainty estimates and biased results because such methods ignore any correlations between parameters. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realisation of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. Thi...
Deep Learning and Bayesian Methods
Prosper, Harrison B.
2017-03-01
A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Bayesian approach to rough set
Marwala, Tshilidzi
2007-01-01
This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance criteria. The proposed method is tested to estimate the risk of HIV given demographic data. The results obtained shows that the proposed approach is able to achieve an average accuracy of 58% with the accuracy varying up to 66%. In addition the Bayesian rough set give the probabilities of the estimated HIV status as well as the linguistic rules describing how the demographic parameters drive the risk of HIV.
BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS
Directory of Open Access Journals (Sweden)
Thordis Linda Thorarinsdottir
2011-05-01
Full Text Available In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.
Bayesian Source Separation and Localization
Knuth, K H
1998-01-01
The problem of mixed signals occurs in many different contexts; one of the most familiar being acoustics. The forward problem in acoustics consists of finding the sound pressure levels at various detectors resulting from sound signals emanating from the active acoustic sources. The inverse problem consists of using the sound recorded by the detectors to separate the signals and recover the original source waveforms. In general, the inverse problem is unsolvable without additional information. This general problem is called source separation, and several techniques have been developed that utilize maximum entropy, minimum mutual information, and maximum likelihood. In previous work, it has been demonstrated that these techniques can be recast in a Bayesian framework. This paper demonstrates the power of the Bayesian approach, which provides a natural means for incorporating prior information into a source model. An algorithm is developed that utilizes information regarding both the statistics of the amplitudes...
Deep Learning and Bayesian Methods
Directory of Open Access Journals (Sweden)
Prosper Harrison B.
2017-01-01
Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Bayesian priors for transiting planets
Kipping, David M
2016-01-01
As astronomers push towards discovering ever-smaller transiting planets, it is increasingly common to deal with low signal-to-noise ratio (SNR) events, where the choice of priors plays an influential role in Bayesian inference. In the analysis of exoplanet data, the selection of priors is often treated as a nuisance, with observers typically defaulting to uninformative distributions. Such treatments miss a key strength of the Bayesian framework, especially in the low SNR regime, where even weak a priori information is valuable. When estimating the parameters of a low-SNR transit, two key pieces of information are known: (i) the planet has the correct geometric alignment to transit and (ii) the transit event exhibits sufficient signal-to-noise to have been detected. These represent two forms of observational bias. Accordingly, when fitting transits, the model parameter priors should not follow the intrinsic distributions of said terms, but rather those of both the intrinsic distributions and the observational ...
Bayesian inference on proportional elections.
Directory of Open Access Journals (Sweden)
Gabriel Hideki Vatanabe Brunello
Full Text Available Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.
Elements of Bayesian experimental design
Energy Technology Data Exchange (ETDEWEB)
Sivia, D.S. [Rutherford Appleton Lab., Oxon (United Kingdom)
1997-09-01
We consider some elements of the Bayesian approach that are important for optimal experimental design. While the underlying principles used are very general, and are explained in detail in a recent tutorial text, they are applied here to the specific case of characterising the inferential value of different resolution peakshapes. This particular issue was considered earlier by Silver, Sivia and Pynn (1989, 1990a, 1990b), and the following presentation confirms and extends the conclusions of their analysis.
Space Shuttle RTOS Bayesian Network
Morris, A. Terry; Beling, Peter A.
2001-01-01
With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores
Multiview Bayesian Correlated Component Analysis
DEFF Research Database (Denmark)
Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai
2015-01-01
we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....... are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...
Bayesian analysis for kaon photoproduction
Energy Technology Data Exchange (ETDEWEB)
Marsainy, T., E-mail: tmart@fisika.ui.ac.id; Mart, T., E-mail: tmart@fisika.ui.ac.id [Department Fisika, FMIPA, Universitas Indonesia, Depok 16424 (Indonesia)
2014-09-25
We have investigated contribution of the nucleon resonances in the kaon photoproduction process by using an established statistical decision making method, i.e. the Bayesian method. This method does not only evaluate the model over its entire parameter space, but also takes the prior information and experimental data into account. The result indicates that certain resonances have larger probabilities to contribute to the process.
Bayesian priors and nuisance parameters
Gupta, Sourendu
2016-01-01
Bayesian techniques are widely used to obtain spectral functions from correlators. We suggest a technique to rid the results of nuisance parameters, ie, parameters which are needed for the regularization but cannot be determined from data. We give examples where the method works, including a pion mass extraction with two flavours of staggered quarks at a lattice spacing of about 0.07 fm. We also give an example where the method does not work.
Bayesian kinematic earthquake source models
Minson, S. E.; Simons, M.; Beck, J. L.; Genrich, J. F.; Galetzka, J. E.; Chowdhury, F.; Owen, S. E.; Webb, F.; Comte, D.; Glass, B.; Leiva, C.; Ortega, F. H.
2009-12-01
Most coseismic, postseismic, and interseismic slip models are based on highly regularized optimizations which yield one solution which satisfies the data given a particular set of regularizing constraints. This regularization hampers our ability to answer basic questions such as whether seismic and aseismic slip overlap or instead rupture separate portions of the fault zone. We present a Bayesian methodology for generating kinematic earthquake source models with a focus on large subduction zone earthquakes. Unlike classical optimization approaches, Bayesian techniques sample the ensemble of all acceptable models presented as an a posteriori probability density function (PDF), and thus we can explore the entire solution space to determine, for example, which model parameters are well determined and which are not, or what is the likelihood that two slip distributions overlap in space. Bayesian sampling also has the advantage that all a priori knowledge of the source process can be used to mold the a posteriori ensemble of models. Although very powerful, Bayesian methods have up to now been of limited use in geophysical modeling because they are only computationally feasible for problems with a small number of free parameters due to what is called the "curse of dimensionality." However, our methodology can successfully sample solution spaces of many hundreds of parameters, which is sufficient to produce finite fault kinematic earthquake models. Our algorithm is a modification of the tempered Markov chain Monte Carlo (tempered MCMC or TMCMC) method. In our algorithm, we sample a "tempered" a posteriori PDF using many MCMC simulations running in parallel and evolutionary computation in which models which fit the data poorly are preferentially eliminated in favor of models which better predict the data. We present results for both synthetic test problems as well as for the 2007 Mw 7.8 Tocopilla, Chile earthquake, the latter of which is constrained by InSAR, local high
Bayesian Sampling using Condition Indicators
DEFF Research Database (Denmark)
Faber, Michael H.; Sørensen, John Dalsgaard
2002-01-01
. This allows for a Bayesian formulation of the indicators whereby the experience and expertise of the inspection personnel may be fully utilized and consistently updated as frequentistic information is collected. The approach is illustrated on an example considering a concrete structure subject to corrosion....... It is shown how half-cell potential measurements may be utilized to update the probability of excessive repair after 50 years....
Naive T cell homeostasis: from awareness of space to a sense of place.
Takada, Kensuke; Jameson, Stephen C
2009-12-01
The peripheral naive T cell pool is fairly stable in number, diversity and functional competence in the absence of vigorous immune responses. However, this apparent tranquility is not an intrinsic property of T cells but involves continuous tuning of the T cell pool composition by homeostatic signals. In the past decade, studies have revealed that naive T cells rely on combinatorial signals from self-peptide-MHC complexes and interleukin-7 for their physical and functional maintenance. Competition for these factors dictates T cell 'space'. In addition, recent studies show that these and other homeostatic factors are offered to T cells on stromal cell networks, which also serve to guide T cell trafficking in secondary lymphoid organs. Such findings suggest the importance of 'place' in the perception and integration of homeostatic cues for the maintenance and functional tuning of the naive T cell pool.
Hippocampal and caudate volume reductions in antipsychotic-naive first-episode schizophrenia
DEFF Research Database (Denmark)
Ebdrup, Bjørn Hylsebeck; Glenthøj, Birte; Rasmussen, Hans
2010-01-01
to be influenced by a history of substance abuse. Exploratory analyses indicated reduced volume of the nucleus accumbens in patients with first-episode schizophrenia. LIMITATIONS: This study was not a priori designed to test for differences between schizophrenia patients with or without lifetime substance abuse......BACKGROUND: Enlarged ventricles and reduced hippocampal volume are consistently found in patients with first-episode schizophrenia. Studies investigating brain structure in antipsychotic-naive patients have generally focused on the striatum. In this study, we examined whether ventricular...... enlargement and hippocampal and caudate volume reductions are morphological traits of antipsychotic-naive first-episode schizophrenia. METHODS: We obtained high-resolution 3-dimensional T1-weighted magnetic resonance imaging scans for 38 antipsychotic-naive first-episode schizophrenia patients and 43 matched...
Bayesian second law of thermodynamics.
Bartolotta, Anthony; Carroll, Sean M; Leichenauer, Stefan; Pollack, Jason
2016-08-01
We derive a generalization of the second law of thermodynamics that uses Bayesian updates to explicitly incorporate the effects of a measurement of a system at some point in its evolution. By allowing an experimenter's knowledge to be updated by the measurement process, this formulation resolves a tension between the fact that the entropy of a statistical system can sometimes fluctuate downward and the information-theoretic idea that knowledge of a stochastically evolving system degrades over time. The Bayesian second law can be written as ΔH(ρ_{m},ρ)+〈Q〉_{F|m}≥0, where ΔH(ρ_{m},ρ) is the change in the cross entropy between the original phase-space probability distribution ρ and the measurement-updated distribution ρ_{m} and 〈Q〉_{F|m} is the expectation value of a generalized heat flow out of the system. We also derive refined versions of the second law that bound the entropy increase from below by a non-negative number, as well as Bayesian versions of integral fluctuation theorems. We demonstrate the formalism using simple analytical and numerical examples.
Bayesian second law of thermodynamics
Bartolotta, Anthony; Carroll, Sean M.; Leichenauer, Stefan; Pollack, Jason
2016-08-01
We derive a generalization of the second law of thermodynamics that uses Bayesian updates to explicitly incorporate the effects of a measurement of a system at some point in its evolution. By allowing an experimenter's knowledge to be updated by the measurement process, this formulation resolves a tension between the fact that the entropy of a statistical system can sometimes fluctuate downward and the information-theoretic idea that knowledge of a stochastically evolving system degrades over time. The Bayesian second law can be written as Δ H (ρm,ρ ) + F |m≥0 , where Δ H (ρm,ρ ) is the change in the cross entropy between the original phase-space probability distribution ρ and the measurement-updated distribution ρm and F |m is the expectation value of a generalized heat flow out of the system. We also derive refined versions of the second law that bound the entropy increase from below by a non-negative number, as well as Bayesian versions of integral fluctuation theorems. We demonstrate the formalism using simple analytical and numerical examples.
A review of learning vector quantization classifiers
Nova, David
2015-01-01
In this work we present a review of the state of the art of Learning Vector Quantization (LVQ) classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to date. The main concepts associated with modern LVQ approaches are defined. A comparison is made among eleven LVQ classifiers using one real-world and two artificial datasets.
Deconvolution When Classifying Noisy Data Involving Transformations
Carroll, Raymond
2012-09-01
In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.
Emergence of an NNRTI resistance mutation Y181C in an HIV-infected NNRTI-naive patient.
Magiorkinis, Emmanouil; Paraskevis, Dimitrios; Sambatakou, Helen; Gargalianos, Panagiotis; Haida, Caterina; Vassilakis, Alexandros; Hatzakis, Angelos
2008-03-01
The purpose of our study was to examine the emergence of the Y181C resistance mutation in an NNRTI-naive subject (index patient) at different time points. Phylogenetic trees in protease (PR) and partial reverse transcriptase (RT) regions were inferred by the maximum likelihood (ML) method. The Y181C mutation was detected for the first time when the patient was receiving d4T + ddI + LPV/r; the previous drug combination was 3TC + AZT + IDV. The particular mutation (Y181C) was not present at any time point during the treatment period with 3TC + AZT + IDV. Moreover, there was no evidence of resistance mutations in RT before the initiation of antiretroviral therapy. Phylogenetic analysis including sequences from the index patient and his spouse sampled at different time points, as well as control sequences belonging to the same HIV-1 subtype, revealed that there is no evidence of coinfection or reinfection with Y181C resistance strains, while the virus for both subjects was classified as subtype CRF14_BG. Overall, our findings suggest that the Y181C resistance mutation may be selected, not only by NNRTIs, but also by d4T. This may be of particular significance in developing countries where treatment with Triomune, a fixed combination of d4T, ddI, and nevirapine, is common. The genetic barrier against resistance of this combination may be lower than previously thought.
12th Brazilian Meeting on Bayesian Statistics
Louzada, Francisco; Rifo, Laura; Stern, Julio; Lauretto, Marcelo
2015-01-01
Through refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesia...
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan
2004-01-01
We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating...... and differentiating these circuits in time linear in their size. We report on experimental results showing the successful compilation, and efficient inference, on relational Bayesian networks whose {\\primula}--generated propositional instances have thousands of variables, and whose jointrees have clusters...
Anomaly Detection and Attribution Using Bayesian Networks
2014-06-01
UNCLASSIFIED Anomaly Detection and Attribution Using Bayesian Networks Andrew Kirk, Jonathan Legg and Edwin El-Mahassni National Security and...detection in Bayesian networks , en- abling both the detection and explanation of anomalous cases in a dataset. By exploiting the structure of a... Bayesian network , our algorithm is able to efficiently search for local maxima of data conflict between closely related vari- ables. Benchmark tests using
The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity but MoA classification in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity mode of action using a recently published dataset contain...
The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently pu...
3rd Bayesian Young Statisticians Meeting
Lanzarone, Ettore; Villalobos, Isadora; Mattei, Alessandra
2017-01-01
This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).
SYNTHESIZED EXPECTED BAYESIAN METHOD OF PARAMETRIC ESTIMATE
Institute of Scientific and Technical Information of China (English)
Ming HAN; Yuanyao DING
2004-01-01
This paper develops a new method of parametric estimate, which is named as "synthesized expected Bayesian method". When samples of products are tested and no failure events occur, thedefinition of expected Bayesian estimate is introduced and the estimates of failure probability and failure rate are provided. After some failure information is introduced by making an extra-test, a synthesized expected Bayesian method is defined and used to estimate failure probability, failure rateand some other parameters in exponential distribution and Weibull distribution of populations. Finally,calculations are performed according to practical problems, which show that the synthesized expected Bayesian method is feasible and easy to operate.
Variational bayesian method of estimating variance components.
Arakawa, Aisaku; Taniguchi, Masaaki; Hayashi, Takeshi; Mikawa, Satoshi
2016-07-01
We developed a Bayesian analysis approach by using a variational inference method, a so-called variational Bayesian method, to determine the posterior distributions of variance components. This variational Bayesian method and an alternative Bayesian method using Gibbs sampling were compared in estimating genetic and residual variance components from both simulated data and publically available real pig data. In the simulated data set, we observed strong bias toward overestimation of genetic variance for the variational Bayesian method in the case of low heritability and low population size, and less bias was detected with larger population sizes in both methods examined. The differences in the estimates of variance components between the variational Bayesian and the Gibbs sampling were not found in the real pig data. However, the posterior distributions of the variance components obtained with the variational Bayesian method had shorter tails than those obtained with the Gibbs sampling. Consequently, the posterior standard deviations of the genetic and residual variances of the variational Bayesian method were lower than those of the method using Gibbs sampling. The computing time required was much shorter with the variational Bayesian method than with the method using Gibbs sampling.
Learning dynamic Bayesian networks with mixed variables
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learn....... An automated procedure for specifying prior distributions for the parameters in a dynamic Bayesian network is presented. It is a simple extension of the procedure for the ordinary Bayesian networks. Finally the W¨olfer?s sunspot numbers are analyzed....
Logarithmic learning for generalized classifier neural network.
Ozyildirim, Buse Melis; Avci, Mutlu
2014-12-01
Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.
User-customized brain computer interfaces using Bayesian optimization
Bashashati, Hossein; Ward, Rabab K.; Bashashati, Ali
2016-04-01
Objective. The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject’s brain characteristics. Approach. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. Main Results. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Significance. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.
A Sequential Algorithm for Training Text Classifiers
Lewis, D D; Lewis, David D.; Gale, William A.
1994-01-01
The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.
A CLASSIFIER SYSTEM USING SMOOTH GRAPH COLORING
Directory of Open Access Journals (Sweden)
JORGE FLORES CRUZ
2017-01-01
Full Text Available Unsupervised classifiers allow clustering methods with less or no human intervention. Therefore it is desirable to group the set of items with less data processing. This paper proposes an unsupervised classifier system using the model of soft graph coloring. This method was tested with some classic instances in the literature and the results obtained were compared with classifications made with human intervention, yielding as good or better results than supervised classifiers, sometimes providing alternative classifications that considers additional information that humans did not considered.
Cohen Stuart, James; Hamann, Dörte; Borleffs, Jan; Roos, Marijke; Miedema, Frank; Boucher, Charles; de Boer, Rob
2002-01-01
OBJECTIVE: To determine the influence of age on the regeneration rate of naive and memory T cells in the blood of 45 adults on highly active antiretroviral therapy (HAART). METHODS: The age of the patients ranged from 25 to 57 years. Naive cells were defined as CD45RA+CD27+. Cells negative for CD45R
Bayesian flood forecasting methods: A review
Han, Shasha; Coulibaly, Paulin
2017-08-01
Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been
Genetic fuzzy classifier for sleep stage identification.
Jo, Han G; Park, Jin Y; Lee, Chung K; An, Suk K; Yoo, Sun K
2010-07-01
Soft-computing techniques are commonly used to detect medical phenomena and help with clinical diagnoses and treatment. In this work, we propose a design for a computerized sleep scoring method, which is based on a fuzzy classifier and a genetic algorithm (GA). We design the fuzzy classifier based on the GA using a single electroencephalogram (EEG) signal that detects differences in spectral features. Polysomnography was performed on four healthy young adults (males with a mean age of 27.5 years). The sleep classifier was designed using a sleep record and tested on the sleep records of the subjects. Our results show that the genetic fuzzy classifier (GFC) agreed with visual sleep staging approximately 84.6% of the time in detection of wakefulness (WA), shallow sleep (SS), deep sleep (DS), and rapid eye movement (REM) stages.
Local Component Analysis for Nonparametric Bayes Classifier
Khademi, Mahmoud; safayani, Meharn
2010-01-01
The decision boundaries of Bayes classifier are optimal because they lead to maximum probability of correct decision. It means if we knew the prior probabilities and the class-conditional densities, we could design a classifier which gives the lowest probability of error. However, in classification based on nonparametric density estimation methods such as Parzen windows, the decision regions depend on the choice of parameters such as window width. Moreover, these methods suffer from curse of dimensionality of the feature space and small sample size problem which severely restricts their practical applications. In this paper, we address these problems by introducing a novel dimension reduction and classification method based on local component analysis. In this method, by adopting an iterative cross-validation algorithm, we simultaneously estimate the optimal transformation matrices (for dimension reduction) and classifier parameters based on local information. The proposed method can classify the data with co...
An Efficient and Effective Immune Based Classifier
Directory of Open Access Journals (Sweden)
Shahram Golzari
2011-01-01
Full Text Available Problem statement: Artificial Immune Recognition System (AIRS is most popular and effective immune inspired classifier. Resource competition is one stage of AIRS. Resource competition is done based on the number of allocated resources. AIRS uses a linear method to allocate resources. The linear resource allocation increases the training time of classifier. Approach: In this study, a new nonlinear resource allocation method is proposed to make AIRS more efficient. New algorithm, AIRS with proposed nonlinear method, is tested on benchmark datasets from UCI machine learning repository. Results: Based on the results of experiments, using proposed nonlinear resource allocation method decreases the training time and number of memory cells and doesn't reduce the accuracy of AIRS. Conclusion: The proposed classifier is an efficient and effective classifier.
Combining multiple classifiers for age classification
CSIR Research Space (South Africa)
Van Heerden, C
2009-11-01
Full Text Available The authors compare several different classifier combination methods on a single task, namely speaker age classification. This task is well suited to combination strategies, since significantly different feature classes are employed. Support vector...
Classifiers based on optimal decision rules
Amin, Talha
2013-11-25
Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).
Pragmatics of classifier use in Chinese discourse
African Journals Online (AJOL)
KATEVG
complex noun phrases (CNPs), and investigates the occurrence and ... classifier phrase from its head noun while a post-nominal RC in English does not ...... The present study takes a cognitive-functional approach to the analysis of a syntactic.
Children's Naive Theories of Intelligence Influence Their Metacognitive Judgments
Miele, David B.; Son, Lisa K.; Metcalfe, Janet
2013-01-01
Recent studies have shown that the metacognitive judgments adults infer from their experiences of encoding effort vary in accordance with their naive theories of intelligence. To determine whether this finding extends to elementary schoolchildren, a study was conducted in which 27 third graders (M[subscript age] = 8.27) and 24 fifth graders…
Anterior Cingulate Volumetric Alterations in Treatment-Naive Adults with ADHD: A Pilot Study
Makris, Nikos; Seidman, Larry J.; Valera, Eve M.; Biederman, Joseph; Monuteaux, Michael C.; Kennedy, David N.; Caviness, Verne S., Jr.; Bush, George; Crum, Katherine; Brown, Ariel B.; Faraone, Stephen V.
2010-01-01
Objective: We sought to examine preliminary results of brain alterations in anterior cingulate cortex (ACC) in treatment-naive adults with ADHD. The ACC is a central brain node for the integration of cognitive control and allocation of attention, affect and drive. Thus its anatomical alteration may give rise to impulsivity, hyperactivity and…
The Effect of Naive Ideas on Students' Reasoning about Electricity and Magnetism
Leppavirta, Johanna
2012-01-01
Traditional multiple-choice concept inventories measure students' critical conceptual understanding and are designed to reveal students' naive or alternate ideas. The overall scores, however, give little information about the state of students' knowledge and the consistency of reasoning. This study investigates whether students have consistent…
DEFF Research Database (Denmark)
Krakowski, M L; Owens, T
2000-01-01
shown, although many studies have shown extravasation of activated or memory T cells. We have used a novel experimental system to track naive T cells to the central nervous system (CNS) in TCR transgenic mice with adoptively transferred experimental autoimmune encephalomyelitis. Ovalbumin (OVA...
Personality matters: individual variation in reactions of naive bird predators to aposematic prey
Exnerová, A.; Hotova Svadova, K.; Fucikova, E.; Drent, P.; Stys, P.
2010-01-01
Variation in reactions to aposematic prey is common among conspecific individuals of bird predators. It may result from different individual experience but it also exists among naive birds. This variation may possibly be explained by the effect of personality—a complex of correlated, heritable
Observation of the naive-T-odd Sivers effect in deep-inelastic scattering.
Airapetian, A; Akopov, N; Akopov, Z; Aschenauer, E C; Augustyniak, W; Avetissian, A; Avetisyan, E; Bacchetta, A; Ball, B; Bianchi, N; Blok, H P; Böttcher, H; Bonomo, C; Borissov, A; Bryzgalov, V; Burns, J; Capiluppi, M; Capitani, G P; Cisbani, E; Ciullo, G; Contalbrigo, M; Dalpiaz, P F; Deconinck, W; De Leo, R; De Nardo, L; De Sanctis, E; Diefenthaler, M; Di Nezza, P; Dreschler, J; Düren, M; Ehrenfried, M; Elbakian, G; Ellinghaus, F; Elschenbroich, U; Fabbri, R; Fantoni, A; Felawka, L; Frullani, S; Gabbert, D; Gapienko, G; Gapienko, V; Garibaldi, F; Gharibyan, V; Giordano, F; Gliske, S; Hadjidakis, C; Hartig, M; Hasch, D; Hill, G; Hillenbrand, A; Hoek, M; Holler, Y; Hristova, I; Imazu, Y; Ivanilov, A; Jackson, H E; Jo, H S; Joosten, S; Kaiser, R; Keri, T; Kinney, E; Kisselev, A; Korotkov, V; Kozlov, V; Kravchenko, P; Lagamba, L; Lamb, R; Lapikás, L; Lehmann, I; Lenisa, P; Linden-Levy, L A; López Ruiz, A; Lorenzon, W; Lu, X-G; Lu, X-R; Ma, B-Q; Mahon, D; Makins, N C R; Manaenkov, S I; Manfré, L; Mao, Y; Marianski, B; Martinez de la Ossa, A; Marukyan, H; Miller, C A; Miyachi, Y; Movsisyan, A; Murray, M; Mussgiller, A; Nappi, E; Naryshkin, Y; Nass, A; Negodaev, M; Nowak, W-D; Pappalardo, L L; Perez-Benito, R; Reimer, P E; Reolon, A R; Riedl, C; Rith, K; Rosner, G; Rostomyan, A; Rubin, J; Ryckbosch, D; Salomatin, Y; Sanftl, F; Schäfer, A; Schnell, G; Schüler, K P; Seitz, B; Shibata, T-A; Shutov, V; Stancari, M; Statera, M; Steijger, J J M; Stenzel, H; Stewart, J; Stinzing, F; Taroian, S; Terkulov, A; Trzcinski, A; Tytgat, M; Vandenbroucke, A; van der Nat, P B; Van Haarlem, Y; Van Hulse, C; Varanda, M; Veretennikov, D; Vikhrov, V; Vilardi, I; Vogel, C; Wang, S; Yaschenko, S; Ye, H; Ye, Z; Yen, S; Yu, W; Zeiler, D; Zihlmann, B; Zupranski, P
2009-10-09
Azimuthal single-spin asymmetries of leptoproduced pions and charged kaons were measured on a transversely polarized hydrogen target. Evidence for a naive-T-odd, transverse-momentum-dependent parton distribution function is deduced from nonvanishing Sivers effects for pi(+), pi(0), and K(+/-), as well as in the difference of the pi(+) and pi(-) cross sections.
DEFF Research Database (Denmark)
Krakowski, M L; Owens, T
2000-01-01
Organ-specific autoimmune diseases may be induced by infiltration of the target tissue by CD4(+) T cells with specificity for self antigen(s). As disease progresses, T cells of other specificities appear in the tissue. Traffic of naive, antigen-inexperienced T cells to target tissues has not been...
Personality matters: individual variation in reactions of naive bird predators to aposematic prey
Exnerová, A.; Hotova Svadova, K.; Fucikova, E.; Drent, P.; Stys, P.
2010-01-01
Variation in reactions to aposematic prey is common among conspecific individuals of bird predators. It may result from different individual experience but it also exists among naive birds. This variation may possibly be explained by the effect of personality—a complex of correlated, heritable behav
Cicin-Sain, Luka; Smyk-Pearson, Susan; Smyk-Paerson, Sue; Currier, Noreen; Byrd, Laura; Koudelka, Caroline; Robinson, Tammie; Swarbrick, Gwendolyn; Tackitt, Shane; Legasse, Alfred; Fischer, Miranda; Nikolich-Zugich, Dragana; Park, Byung; Hobbs, Theodore; Doane, Cynthia J; Mori, Motomi; Axthelm, Michael K; Axthelm, Michael T; Lewinsohn, Deborah A; Nikolich-Zugich, Janko
2010-06-15
Aging is usually accompanied by diminished immune protection upon infection or vaccination. Although aging results in well-characterized changes in the T cell compartment of long-lived, outbred, and pathogen-exposed organisms, their relevance for primary Ag responses remain unclear. Therefore, it remains unclear whether and to what extent the loss of naive T cells, their partial replacement by oligoclonal memory populations, and the consequent constriction of TCR repertoire limit the Ag responses in aging primates. We show in this study that aging rhesus monkeys (Macaca mulatta) exhibit poor CD8 T cell and B cell responses in the blood and poor CD8 responses in the lungs upon vaccination with the modified vaccinia strain Ankara. The function of APCs appeared to be maintained in aging monkeys, suggesting that the poor response was likely intrinsic to lymphocytes. We found that the loss of naive CD4 and CD8 T cells, and the appearance of persisting T cell clonal expansions predicted poor CD8 responses in individual monkeys. There was strong correlation between early CD8 responses in the transitory CD28+ CD62L- CD8+ T cell compartment and the peak Ab titers upon boost in individual animals, as well as a correlation of both parameters of immune response to the frequency of naive CD8+ T cells in old but not in adult monkeys. Therefore, our results argue that T cell repertoire constriction and naive cell loss have prognostic value for global immune function in aging primates.
Erk5 Is a Key Regulator of Naive-Primed Transition and Embryonic Stem Cell Identity
Directory of Open Access Journals (Sweden)
Charles A.C. Williams
2016-08-01
Full Text Available Embryonic stem cells (ESCs can self-renew or differentiate into any cell type, a phenomenon known as pluripotency. Distinct pluripotent states, termed naive and primed pluripotency, have been described. However, the mechanisms that control naive-primed pluripotent transition are poorly understood. Here, we perform a targeted screen for kinase inhibitors, which modulate the naive-primed pluripotent transition. We find that XMD compounds, which selectively inhibit Erk5 kinase and BET bromodomain family proteins, drive ESCs toward primed pluripotency. Using compound selectivity engineering and CRISPR/Cas9 genome editing, we reveal distinct functions for Erk5 and Brd4 in pluripotency regulation. We show that Erk5 signaling maintains ESCs in the naive state and suppresses progression toward primed pluripotency and neuroectoderm differentiation. Additionally, we identify a specialized role for Erk5 in defining ESC lineage selection, whereby Erk5 inhibits a cardiomyocyte-specific differentiation program. Our data therefore reveal multiple critical functions for Erk5 in controlling ESC identity.
Test of the Universality of Naive-Time-Reversal-Odd Fragmentation Functions
Boer, Daniel; Kang, Zhong-Bo; Vogelsang, Werner; Yuan, Feng
2010-01-01
We investigate the "spontaneous'' hyperon transverse polarization in e(+)e(-) annihilation and semi-inclusive deep inelastic scattering processes as a test of the universality of the naive-time-reversal-odd transverse momentum dependent fragmentation functions. We find that universality implies defi
Hilkhuysen, Gaston L. M.; Gaubitch, Nikolay; Huckvale, Mark
2013-01-01
Purpose: In this study, the authors investigated how well experts can adjust the settings of a commercial noise-reduction system to optimize the intelligibility for naive normal-hearing listeners. Method: In Experiment 1, 5 experts adjusted parameters for a noise-reduction system while aiming to optimize intelligibility. The stimuli consisted of…
Bayesian Methods and Universal Darwinism
Campbell, John
2009-12-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent Champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a `copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the Operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that Systems will evolve to states of highest entropy subject to the constraints of scientific law. This principle may be inverted to provide illumination as to the nature of scientific law. Our best cosmological theories suggest the universe contained much less complexity during the period shortly after the Big Bang than it does at present. The scientific subject matter of atomic physics, chemistry, biology and the social sciences has been created since that time. An explanation is proposed for the existence of this subject matter as due to the evolution of constraints in the form of adaptations imposed on Maximum Entropy. It is argued these adaptations were discovered and instantiated through the Operations of a succession of Darwinian processes.
Bayesian phylogeography finds its roots.
Directory of Open Access Journals (Sweden)
Philippe Lemey
2009-09-01
Full Text Available As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.
Classifying the Quantum Phases of Matter
2015-01-01
2013), arXiv:1305.2176. [10] J. Haah, Lattice quantum codes and exotic topological phases of matter , arXiv:1305.6973. [11[ M. Hastings and S...CLASSIFYING THE QUANTUM PHASES OF MATTER CALIFORNIA INSTITUTE OF TECHNOLOGY JANUARY 2015 FINAL TECHNICAL REPORT...REPORT 3. DATES COVERED (From - To) JAN 2012 – AUG 2014 4. TITLE AND SUBTITLE CLASSIFYING THE QUANTUM PHASES OF MATTER 5a. CONTRACT NUMBER FA8750-12-2
Classifying Genomic Sequences by Sequence Feature Analysis
Institute of Scientific and Technical Information of China (English)
Zhi-Hua Liu; Dian Jiao; Xiao Sun
2005-01-01
Traditional sequence analysis depends on sequence alignment. In this study, we analyzed various functional regions of the human genome based on sequence features, including word frequency, dinucleotide relative abundance, and base-base correlation. We analyzed the human chromosome 22 and classified the upstream,exon, intron, downstream, and intergenic regions by principal component analysis and discriminant analysis of these features. The results show that we could classify the functional regions of genome based on sequence feature and discriminant analysis.
Searching and Classifying non-textual information
Arentz, Will Archer
2004-01-01
This dissertation contains a set of contributions that deal with search or classification of non-textual information. Each contribution can be considered a solution to a specific problem, in an attempt to map out a common ground. The problems cover a wide range of research fields, including search in music, classifying digitally sampled music, visualization and navigation in search results, and classifying images and Internet sites.On classification of digitally sample music, as method for ex...
Bayesian homeopathy: talking normal again.
Rutten, A L B
2007-04-01
Homeopathy has a communication problem: important homeopathic concepts are not understood by conventional colleagues. Homeopathic terminology seems to be comprehensible only after practical experience of homeopathy. The main problem lies in different handling of diagnosis. In conventional medicine diagnosis is the starting point for randomised controlled trials to determine the effect of treatment. In homeopathy diagnosis is combined with other symptoms and personal traits of the patient to guide treatment and predict response. Broadening our scope to include diagnostic as well as treatment research opens the possibility of multi factorial reasoning. Adopting Bayesian methodology opens the possibility of investigating homeopathy in everyday practice and of describing some aspects of homeopathy in conventional terms.
Approximation for Bayesian Ability Estimation.
1987-02-18
posterior pdfs of ande are given by p(-[Y) p(F) F P((y lei’ j)P )d. SiiJ i (4) a r~d p(e Iy) - p(t0) 1 J i P(Yij ei, (5) As shown in Tsutakawa and Lin...inverse A Hessian of the log of (27) with respect to , evaulatedat a Then, under regularity conditions, the marginal posterior pdf of O is...two-way contingency tables. Journal of Educational Statistics, 11, 33-56. Lindley, D.V. (1980). Approximate Bayesian methods. Trabajos Estadistica , 31
Bayesian Query-Focused Summarization
Daumé, Hal
2009-01-01
We present BayeSum (for ``Bayesian summarization''), a model for sentence extraction in query-focused summarization. BayeSum leverages the common case in which multiple documents are relevant to a single query. Using these documents as reinforcement for query terms, BayeSum is not afflicted by the paucity of information in short queries. We show that approximate inference in BayeSum is possible on large data sets and results in a state-of-the-art summarization system. Furthermore, we show how BayeSum can be understood as a justified query expansion technique in the language modeling for IR framework.
Bayesian tests of measurement invariance.
Verhagen, A J; Fox, J P
2013-11-01
Random item effects models provide a natural framework for the exploration of violations of measurement invariance without the need for anchor items. Within the random item effects modelling framework, Bayesian tests (Bayes factor, deviance information criterion) are proposed which enable multiple marginal invariance hypotheses to be tested simultaneously. The performance of the tests is evaluated with a simulation study which shows that the tests have high power and low Type I error rate. Data from the European Social Survey are used to test for measurement invariance of attitude towards immigrant items and to show that background information can be used to explain cross-national variation in item functioning.
Numeracy, frequency, and Bayesian reasoning
Directory of Open Access Journals (Sweden)
Gretchen B. Chapman
2009-02-01
Full Text Available Previous research has demonstrated that Bayesian reasoning performance is improved if uncertainty information is presented as natural frequencies rather than single-event probabilities. A questionnaire study of 342 college students replicated this effect but also found that the performance-boosting benefits of the natural frequency presentation occurred primarily for participants who scored high in numeracy. This finding suggests that even comprehension and manipulation of natural frequencies requires a certain threshold of numeracy abilities, and that the beneficial effects of natural frequency presentation may not be as general as previously believed.
COMBINING CLASSIFIERS FOR CREDIT RISK PREDICTION
Institute of Scientific and Technical Information of China (English)
Bhekisipho TWALA
2009-01-01
Credit risk prediction models seek to predict quality factors such as whether an individual will default (bad applicant) on a loan or not (good applicant). This can be treated as a kind of machine learning (ML) problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how could such accuracy be improved by using pairs of classifier ensembles. Benchmarking results on five credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy.
A multi-class large margin classifier
Institute of Scientific and Technical Information of China (English)
Liang TANG; Qi XUAN; Rong XIONG; Tie-jun WU; Jian CHU
2009-01-01
Currently there are two approaches for a multi-class support vector classifier (SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem (K>2), the first approach has to construct at least K classifiers, and the second approach has to solve a much larger op-timization problem proportional to K by the algorithms developed so far. In this paper, following the second approach, we present a novel multi-class large margin classifier (MLMC). This new machine can solve K-class problems in one optimization formula-tion without increasing the size of the quadratic programming (QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data, and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as (sometimes better than) many other multi-class SVCs for some benchmark data classification problems, and obtains a reasonable performance in face recognition application on the AR face database.
Dynamic Bayesian Networks in Classification-and-Ranking Architecture of Response Generation
Directory of Open Access Journals (Sweden)
Aida Mustapha
2011-01-01
Full Text Available Problem statement: The first component in classification-and-ranking architecture is a Bayesian classifier that classifies user utterances into response classes based on their semantic and pragmatic interpretations. Bayesian networks are sufficient if data is limited to single user input utterance. However, if the classifier is able to collate features from a sequence of previous n-1 user utterances, the additional information may or may not improve the accuracy rate in response classification. Approach: This article investigates the use of dynamic Bayesian networks to include time-series information in the form of extended features from preceding utterances. The experiment was conducted on SCHISMA corpus, which is a mixed-initiative, transaction dialogue in theater reservation. Results: The results show that classification accuracy is improved, but rather insignificantly. The accuracy rate tends to deteriorate as time-span of dialogue is increased. Conclusion: Although every response utterance reflects form and behavior that are expected by the preceding utterance, influence of meaning and intentions diminishes throughout time as the conversation stretches to longer duration.
Lymphatic endothelial S1P promotes mitochondrial function and survival in naive T cells.
Mendoza, Alejandra; Fang, Victoria; Chen, Cynthia; Serasinghe, Madhavika; Verma, Akanksha; Muller, James; Chaluvadi, V Sai; Dustin, Michael L; Hla, Timothy; Elemento, Olivier; Chipuk, Jerry E; Schwab, Susan R
2017-06-01
Effective adaptive immune responses require a large repertoire of naive T cells that migrate throughout the body, rapidly identifying almost any foreign peptide. Because the production of T cells declines with age, naive T cells must be long-lived. However, it remains unclear how naive T cells survive for years while constantly travelling. The chemoattractant sphingosine 1-phosphate (S1P) guides T cell circulation among secondary lymphoid organs, including spleen, lymph nodes and Peyer's patches, where T cells search for antigens. The concentration of S1P is higher in circulatory fluids than in lymphoid organs, and the S1P1 receptor (S1P1R) directs the exit of T cells from the spleen into blood, and from lymph nodes and Peyer's patches into lymph. Here we show that S1P is essential not only for the circulation of naive T cells, but also for their survival. Using transgenic mouse models, we demonstrate that lymphatic endothelial cells support the survival of T cells by secreting S1P via the transporter SPNS2, that this S1P signals through S1P1R on T cells, and that the requirement for S1P1R is independent of the established role of the receptor in guiding exit from lymph nodes. S1P signalling maintains the mitochondrial content of naive T cells, providing cells with the energy to continue their constant migration. The S1P signalling pathway is being targeted therapeutically to inhibit autoreactive T cell trafficking, and these findings suggest that it may be possible simultaneously to target autoreactive or malignant cell survival.
Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification
Directory of Open Access Journals (Sweden)
Demi Soetraprawata
2013-06-01
Full Text Available Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
Using Bayesian Networks to Improve Knowledge Assessment
Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra
2013-01-01
In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...... for complex networks can be derived and point out relevant literature....
Bayesian analysis of exoplanet and binary orbits
Schulze-Hartung, Tim; Launhardt, Ralf; Henning, Thomas
2012-01-01
We introduce BASE (Bayesian astrometric and spectroscopic exoplanet detection and characterisation tool), a novel program for the combined or separate Bayesian analysis of astrometric and radial-velocity measurements of potential exoplanet hosts and binary stars. The capabilities of BASE are demonstrated using all publicly available data of the binary Mizar A.
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
Bayesian Decision Theoretical Framework for Clustering
Chen, Mo
2011-01-01
In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…
Computational Advances for and from Bayesian Analysis
Andrieu, C.; Doucet, A.; Robert, C. P.
2004-01-01
The emergence in the past years of Bayesian analysis in many methodological and applied fields as the solution to the modeling of complex problems cannot be dissociated from major changes in its computational implementation. We show in this review how the advances in Bayesian analysis and statistical computation are intermingled.
Particle identification in ALICE: a Bayesian approach
Adam, J.; Adamova, D.; Aggarwal, M. M.; Rinella, G. Aglieri; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anticic, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshaeuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badala, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnafoeldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Bathen, B.; Batigne, G.; Camejo, A. Batista; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Benacek, P.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielcik, J.; Bielcikova, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Boggild, H.; Boldizsar, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossu, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Diaz, L. Calero; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castellanos, J. Castillo; Castro, A. J.; Casula, E. A. R.; Sanchez, C. Ceballos; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Barroso, V. Chibante; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Balbastre, G. Conesa; del Valle, Z. Conesa; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Morales, Y. Corrales; Cortes Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Denes, E.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Corchero, M. A. Diaz; Dietel, T.; Dillenseger, P.; Divia, R.; Djuvsland, O.; Dobrin, A.; Gimenez, D. Domenicis; Doenigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernandez Tellez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Girard, M. Fusco; Gaardhoje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, A.; Gheata, M.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glaessel, P.; Gomez Coral, D. M.; Ramirez, A. Gomez; Gonzalez, A. S.; Gonzalez, V.; Gonzalez-Zamora, P.; Gorbunov, S.; Goerlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Haake, R.; Haaland, O.
2016-01-01
We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian
Bayesian credible interval construction for Poisson statistics
Institute of Scientific and Technical Information of China (English)
ZHU Yong-Sheng
2008-01-01
The construction of the Bayesian credible (confidence) interval for a Poisson observable including both the signal and background with and without systematic uncertainties is presented.Introducing the conditional probability satisfying the requirement of the background not larger than the observed events to construct the Bayesian credible interval is also discussed.A Fortran routine,BPOCI,has been developed to implement the calculation.
Using Bayesian Networks to Improve Knowledge Assessment
Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra
2013-01-01
In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…
The Bayesian Revolution Approaches Psychological Development
Shultz, Thomas R.
2007-01-01
This commentary reviews five articles that apply Bayesian ideas to psychological development, some with psychology experiments, some with computational modeling, and some with both experiments and modeling. The reviewed work extends the current Bayesian revolution into tasks often studied in children, such as causal learning and word learning, and…
Advances in Bayesian Modeling in Educational Research
Levy, Roy
2016-01-01
In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…
Bayesian Network for multiple hypthesis tracking
W.P. Zajdel; B.J.A. Kröse
2002-01-01
For a flexible camera-to-camera tracking of multiple objects we model the objects behavior with a Bayesian network and combine it with the multiple hypohesis framework that associates observations with objects. Bayesian networks offer a possibility to factor complex, joint distributions into a produ
Bayesian Statistics for Biological Data: Pedigree Analysis
Stanfield, William D.; Carlton, Matthew A.
2004-01-01
The use of Bayes' formula is applied to the biological problem of pedigree analysis to show that the Bayes' formula and non-Bayesian or "classical" methods of probability calculation give different answers. First year college students of biology can be introduced to the Bayesian statistics.
Hepatitis disease detection using Bayesian theory
Maseleno, Andino; Hidayati, Rohmah Zahroh
2017-02-01
This paper presents hepatitis disease diagnosis using a Bayesian theory for better understanding of the theory. In this research, we used a Bayesian theory for detecting hepatitis disease and displaying the result of diagnosis process. Bayesian algorithm theory is rediscovered and perfected by Laplace, the basic idea is using of the known prior probability and conditional probability density parameter, based on Bayes theorem to calculate the corresponding posterior probability, and then obtained the posterior probability to infer and make decisions. Bayesian methods combine existing knowledge, prior probabilities, with additional knowledge derived from new data, the likelihood function. The initial symptoms of hepatitis which include malaise, fever and headache. The probability of hepatitis given the presence of malaise, fever, and headache. The result revealed that a Bayesian theory has successfully identified the existence of hepatitis disease.
Bayesian natural language semantics and pragmatics
Zeevat, Henk
2015-01-01
The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice's contributions to pragmatics or in interpretation by abduction.
2nd Bayesian Young Statisticians Meeting
Bitto, Angela; Kastner, Gregor; Posekany, Alexandra
2015-01-01
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session ...
BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION.
Khakabimamaghani, Sahand; Ester, Martin
2016-01-01
The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification. Third, as most of the proposed stratification methods are deterministic, there is a need for investigating the potential benefits of applying probabilistic methods. To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. Our experimental results demonstrate the superiority of B2PS over a popular state-of-the-art method and the benefits of Bayesian approaches. Our results agree with the intuition that transcriptomic data forms a better basis for patient stratification than genomic data.
Flexible Bayesian Human Fecundity Models.
Kim, Sungduk; Sundaram, Rajeshwari; Buck Louis, Germaine M; Pyper, Cecilia
2012-12-01
Human fecundity is an issue of considerable interest for both epidemiological and clinical audiences, and is dependent upon a couple's biologic capacity for reproduction coupled with behaviors that place a couple at risk for pregnancy. Bayesian hierarchical models have been proposed to better model the conception probabilities by accounting for the acts of intercourse around the day of ovulation, i.e., during the fertile window. These models can be viewed in the framework of a generalized nonlinear model with an exponential link. However, a fixed choice of link function may not always provide the best fit, leading to potentially biased estimates for probability of conception. Motivated by this, we propose a general class of models for fecundity by relaxing the choice of the link function under the generalized nonlinear model framework. We use a sample from the Oxford Conception Study (OCS) to illustrate the utility and fit of this general class of models for estimating human conception. Our findings reinforce the need for attention to be paid to the choice of link function in modeling conception, as it may bias the estimation of conception probabilities. Various properties of the proposed models are examined and a Markov chain Monte Carlo sampling algorithm was developed for implementing the Bayesian computations. The deviance information criterion measure and logarithm of pseudo marginal likelihood are used for guiding the choice of links. The supplemental material section contains technical details of the proof of the theorem stated in the paper, and contains further simulation results and analysis.
Bayesian modeling in conjoint analysis
Directory of Open Access Journals (Sweden)
Janković-Milić Vesna
2010-01-01
Full Text Available Statistical analysis in marketing is largely influenced by the availability of various types of data. There is sudden increase in the number and types of information available to market researchers in the last decade. In such conditions, traditional statistical methods have limited ability to solve problems related to the expression of market uncertainty. The aim of this paper is to highlight the advantages of bayesian inference, as an alternative approach to classical inference. Multivariate statistic methods offer extremely powerful tools to achieve many goals of marketing research. One of these methods is the conjoint analysis, which provides a quantitative measure of the relative importance of product or service attributes in relation to the other attribute. The application of this method involves interviewing consumers, where they express their preferences, and statistical analysis provides numerical indicators of each attribute utility. One of the main objections to the method of discrete choice in the conjoint analysis is to use this method to estimate the utility only at the aggregate level and by expressing the average utility for all respondents in the survey. Application of hierarchical Bayesian models enables capturing of individual utility ratings for each attribute level.
Bayesian Single-Epoch Photometric Classification of Supernovae
Poznanski, D; Gal-Yam, A; Poznanski, Dovi; Maoz, Dan; Gal-Yam, Avishay
2006-01-01
(abridged) Ongoing supernova (SN) surveys find hundreds of candidates, that require confirmation for their use. Traditional classification based on followup spectroscopy of all candidates is virtually impossible for these large samples. We present an automatic Bayesian classifying algorithm for supernovae, the SN-ABC. We rely solely on single-epoch multiband photometry and host-galaxy (photometric) redshift information to sort SN candidates into the two major types, Ia and core-collapse supernovae. We test the SN-ABC performance on published samples of SNe from the SNLS and GOODS projects that have both broad-band photometry and spectroscopic classification (so the true type is known). The SN-ABC correctly classifies up to 97% (85%) of the type Ia (II-P) SNe in SNLS, and similar fractions of the GOODS SNe, depending on photometric redshift quality. We further test our method on large artificial samples to explore possible biases, and find that, in deep surveys, SNe Ia are best classified at redshifts z >~ 0.6...
Minimum Bayesian error probability-based gene subset selection.
Li, Jian; Yu, Tian; Wei, Jin-Mao
2015-01-01
Sifting functional genes is crucial to the new strategies for drug discovery and prospective patient-tailored therapy. Generally, simply generating gene subset by selecting the top k individually superior genes may obtain an inferior gene combination, for some selected genes may be redundant with respect to some others. In this paper, we propose to select gene subset based on the criterion of minimum Bayesian error probability. The method dynamically evaluates all available genes and sifts only one gene at a time. A gene is selected if its combination with the other selected genes can gain better classification information. Within the generated gene subset, each individual gene is the most discriminative one in comparison with those that classify cancers in the same way as this gene does and different genes are more discriminative in combination than in individual. The genes selected in this way are likely to be functional ones from the system biology perspective, for genes tend to co-regulate rather than regulate individually. Experimental results show that the classifiers induced based on this method are capable of classifying cancers with high accuracy, while only a small number of genes are involved.
Repeatability and classifier bias in computer-aided diagnosis for breast ultrasound
Drukker, K.; Pesce, L. L.; Giger, M. L.
2010-03-01
The purpose was to investigate the repeatability and bias of the output of two classifiers commonly used in computeraided diagnosis for the task of distinguishing benign from malignant lesions. Classifier training and testing were performed within a bootstrap approach using a dataset of 125 sonographic breast lesions (54 malignant, 71 benign). The classifiers investigated were linear discriminant analysis (LDA) and a Bayesian Neural Net (BNN) with 5 hidden units. Both used the same 4 input lesion features. The bootstrap .632plus area under the ROC curve (AUC) was used as a summary performance metric. On an individual case basis, the variability of the classifier output was used in a detailed performance evaluation of repeatability and bias. The LDA obtained an AUC value of 0.87 with 95% confidence interval [0.81; 0.92]. For the BNN, those values were 0.86 and [.76; .93], respectively. The classifier outputs for individual cases displayed better repeatability (less variability) for the LDA than for the BNN and for the LDA the maximum repeatability (lowest variability) lied in the middle of the range of possible outputs, while the BNN was least repeatable (highest variability) in this region. There was a small but significant systematic bias in the LDA output, however, while for the BNN the bias appeared to be weak. In summary, while ROC analysis suggested similar classifier performance, there were substantial differences in classifier behavior on a by-case basis. Knowledge of this behavior is crucial for successful translation and implementation of computer-aided diagnosis in clinical decision making.
Reinforcement Learning Based Artificial Immune Classifier
Directory of Open Access Journals (Sweden)
Mehmet Karakose
2013-01-01
Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.
Evolving Classifiers: Methods for Incremental Learning
Hulley, Greg
2007-01-01
The ability of a classifier to take on new information and classes by evolving the classifier without it having to be fully retrained is known as incremental learning. Incremental learning has been successfully applied to many classification problems, where the data is changing and is not all available at once. In this paper there is a comparison between Learn++, which is one of the most recent incremental learning algorithms, and the new proposed method of Incremental Learning Using Genetic Algorithm (ILUGA). Learn++ has shown good incremental learning capabilities on benchmark datasets on which the new ILUGA method has been tested. ILUGA has also shown good incremental learning ability using only a few classifiers and does not suffer from catastrophic forgetting. The results obtained for ILUGA on the Optical Character Recognition (OCR) and Wine datasets are good, with an overall accuracy of 93% and 94% respectively showing a 4% improvement over Learn++.MT for the difficult multi-class OCR dataset.
A comparison of machine learning and Bayesian modelling for molecular serotyping.
Newton, Richard; Wernisch, Lorenz
2017-08-11
Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological
Adaptive stellar spectral subclass classification based on Bayesian SVMs
Du, Changde; Luo, Ali; Yang, Haifeng
2017-02-01
Stellar spectral classification is one of the most fundamental tasks in survey astronomy. Many automated classification methods have been applied to spectral data. However, their main limitation is that the model parameters must be tuned repeatedly to deal with different data sets. In this paper, we utilize the Bayesian support vector machines (BSVM) to classify the spectral subclass data. Based on Gibbs sampling, BSVM can infer all model parameters adaptively according to different data sets, which allows us to circumvent the time-consuming cross validation for penalty parameter. We explored different normalization methods for stellar spectral data, and the best one has been suggested in this study. Finally, experimental results on several stellar spectral subclass classification problems show that the BSVM model not only possesses good adaptability but also provides better prediction performance than traditional methods.
Visual tracker using sequential bayesian learning: discriminative, generative, and hybrid.
Lei, Yun; Ding, Xiaoqing; Wang, Shengjin
2008-12-01
This paper presents a novel solution to track a visual object under changes in illumination, viewpoint, pose, scale, and occlusion. Under the framework of sequential Bayesian learning, we first develop a discriminative model-based tracker with a fast relevance vector machine algorithm, and then, a generative model-based tracker with a novel sequential Gaussian mixture model algorithm. Finally, we present a three-level hierarchy to investigate different schemes to combine the discriminative and generative models for tracking. The presented hierarchical model combination contains the learner combination (at level one), classifier combination (at level two), and decision combination (at level three). The experimental results with quantitative comparisons performed on many realistic video sequences show that the proposed adaptive combination of discriminative and generative models achieves the best overall performance. Qualitative comparison with some state-of-the-art methods demonstrates the effectiveness and efficiency of our method in handling various challenges during tracking.
A Customizable Text Classifier for Text Mining
Directory of Open Access Journals (Sweden)
Yun-liang Zhang
2007-12-01
Full Text Available Text mining deals with complex and unstructured texts. Usually a particular collection of texts that is specified to one or more domains is necessary. We have developed a customizable text classifier for users to mine the collection automatically. It derives from the sentence category of the HNC theory and corresponding techniques. It can start with a few texts, and it can adjust automatically or be adjusted by user. The user can also control the number of domains chosen and decide the standard with which to choose the texts based on demand and abundance of materials. The performance of the classifier varies with the user's choice.
A survey of decision tree classifier methodology
Safavian, S. R.; Landgrebe, David
1991-01-01
Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.
Bayesian approach to noninferiority trials for proportions.
Gamalo, Mark A; Wu, Rui; Tiwari, Ram C
2011-09-01
Noninferiority trials are unique because they are dependent upon historical information in order to make meaningful interpretation of their results. Hence, a direct application of the Bayesian paradigm in sequential learning becomes apparently useful in the analysis. This paper describes a Bayesian procedure for testing noninferiority in two-arm studies with a binary primary endpoint that allows the incorporation of historical data on an active control via the use of informative priors. In particular, the posteriors of the response in historical trials are assumed as priors for its corresponding parameters in the current trial, where that treatment serves as the active control. The Bayesian procedure includes a fully Bayesian method and two normal approximation methods on the prior and/or on the posterior distributions. Then a common Bayesian decision criterion is used but with two prespecified cutoff levels, one for the approximation methods and the other for the fully Bayesian method, to determine whether the experimental treatment is noninferior to the active control. This criterion is evaluated and compared with the frequentist method using simulation studies in keeping with regulatory framework that new methods must protect type I error and arrive at a similar conclusion with existing standard strategies. Results show that both methods arrive at comparable conclusions of noninferiority when applied to a modified real data set. The advantage of the proposed Bayesian approach lies in its ability to provide posterior probabilities for effect sizes of the experimental treatment over the active control.
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Bayesian networks in educational assessment
Almond, Russell G; Steinberg, Linda S; Yan, Duanli; Williamson, David M
2015-01-01
Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as ...
Elvira, Clément; Dobigeon, Nicolas
2015-01-01
Sparse representations have proven their efficiency in solving a wide class of inverse problems encountered in signal and image processing. Conversely, enforcing the information to be spread uniformly over representation coefficients exhibits relevant properties in various applications such as digital communications. Anti-sparse regularization can be naturally expressed through an $\\ell_{\\infty}$-norm penalty. This paper derives a probabilistic formulation of such representations. A new probability distribution, referred to as the democratic prior, is first introduced. Its main properties as well as three random variate generators for this distribution are derived. Then this probability distribution is used as a prior to promote anti-sparsity in a Gaussian linear inverse problem, yielding a fully Bayesian formulation of anti-sparse coding. Two Markov chain Monte Carlo (MCMC) algorithms are proposed to generate samples according to the posterior distribution. The first one is a standard Gibbs sampler. The seco...
On Bayesian System Reliability Analysis
Energy Technology Data Exchange (ETDEWEB)
Soerensen Ringi, M.
1995-05-01
The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person`s state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs.
Quantum Bayesianism at the Perimeter
Fuchs, Christopher A
2010-01-01
The author summarizes the Quantum Bayesian viewpoint of quantum mechanics, developed originally by C. M. Caves, R. Schack, and himself. It is a view crucially dependent upon the tools of quantum information theory. Work at the Perimeter Institute for Theoretical Physics continues the development and is focused on the hard technical problem of a finding a good representation of quantum mechanics purely in terms of probabilities, without amplitudes or Hilbert-space operators. The best candidate representation involves a mysterious entity called a symmetric informationally complete quantum measurement. Contemplation of it gives a way of thinking of the Born Rule as an addition to the rules of probability theory, applicable when one gambles on the consequences of interactions with physical systems. The article ends by outlining some directions for future work.
Hedging Strategies for Bayesian Optimization
Brochu, Eric; de Freitas, Nando
2010-01-01
Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It is able to do this by sampling the objective using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We describe the method, which we call GP-Hedge, and show that this method almost always outperforms the best individual acquisition function.
Bayesian Inference with Optimal Maps
Moselhy, Tarek A El
2011-01-01
We present a new approach to Bayesian inference that entirely avoids Markov chain simulation, by constructing a map that pushes forward the prior measure to the posterior measure. Existence and uniqueness of a suitable measure-preserving map is established by formulating the problem in the context of optimal transport theory. We discuss various means of explicitly parameterizing the map and computing it efficiently through solution of an optimization problem, exploiting gradient information from the forward model when possible. The resulting algorithm overcomes many of the computational bottlenecks associated with Markov chain Monte Carlo. Advantages of a map-based representation of the posterior include analytical expressions for posterior moments and the ability to generate arbitrary numbers of independent posterior samples without additional likelihood evaluations or forward solves. The optimization approach also provides clear convergence criteria for posterior approximation and facilitates model selectio...
Bayesian Approach to Inverse Problems
2008-01-01
Many scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data.Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise. Regularization is the key concept to solve inverse problems.The goal of this book is to deal with inverse problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation
Bayesian Inference in Queueing Networks
Sutton, Charles
2010-01-01
Modern Web services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are modeled by networks of queues, where one queue models each of the individual computers in the system. A key challenge is that the data is incomplete, because recording detailed information about every request to a heavily used system can require unacceptable overhead. In this paper we develop a Bayesian perspective on queueing models in which the arrival and departure times that are not observed are treated as latent variables. Underlying this viewpoint is the observation that a queueing model defines a deterministic transformation between the data and a set of independent variables called the service times. With this viewpoint in hand, we sample from the posterior distribution over missing data and model...
A Bayesian Reflection on Surfaces
Directory of Open Access Journals (Sweden)
David R. Wolf
1999-10-01
Full Text Available Abstract: The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a continuous object and not representable in a finite manner; the tradeoff between accuracy of representation in terms of information learned, and memory or storage capacity in bits; the approximation of probability distributions so that a maximal amount of information about the object being inferred is preserved; an information theoretic justification for multigrid methodology. The maximally informative field inference framework is described in full generality and denoted the Generalized Kalman Filter. The Generalized Kalman Filter allows the update of field knowledge from previous knowledge at any scale, and new data, to new knowledge at any other scale. An application example instance, the inference of continuous surfaces from measurements (for example, camera image data, is presented.
Martín, M; Echevarría, S; Leyva-Cobián, F; Pereda, I; López-Hoyos, M
2001-12-01
Although several reports have attributed the clinical benefits of highly active antiretroviral therapy (HAART) to a possible immune restoration, long-term data are still scarce and most derive from patients with either advanced or very early stages of HIV infection. In the present study, changes in lymphocyte subsets, activation markers, and adhesion molecules in CD4+ and CD8+ lymphocytes were carefully monitored over a 1-year period in 27 HIV-infected adults at an intermediate stage of HIV infection. Cytokine-producing patterns were also studied. In these patients the HIV viral load disappeared by month 4 of HAART. Only limited immunological changes were observed: an incomplete recovery of naive CD4+ T cells, a less activated state of CD8+ T cells, and a repopulation of IL-2- and IFN-gamma-producing CD4+ T cells. These changes were observed principally in patients with more advanced disease. Furthermore, HIV-infected subjects who had received HAART previously showed less marked immunological changes than antiretroviral-naive individuals. In conclusion, the sustained viral suppression during 1 year of HAART was accompanied by limited immunological recovery at intermediate stages of HIV infection. This finding indicates a need for longer HIV suppression in order to achieve effective recovery of the immune system.
Online Learning a Binary Classifier for Improving Go ogle Image Search Results
Institute of Scientific and Technical Information of China (English)
WAN Yu-Chai; LIU Xia-Bi; HAN Fei-Fei; TONG Kun-Qi; LIU Yu
2014-01-01
It is promising to improve web image search results through exploiting the results0 visual contents for learning a binary classifier which is used to refine the results0 relevance degrees to the given query. This paper proposes an algorithm framework as a solution to this problem and investigates the key issue of training data selection under the framework. The training data selection process is divided into two stages: initial selection for triggering the classifier learning and dynamic selection in the iterations of classifier learning. We investigate two main ways of initial training data selection, including clustering based and ranking based, and compare automatic training data selection schemes with manual manner. Furthermore, support vector machines and the max-min pseudo-probability (MMP) based Bayesian classifier are employed to support image classification, respectively. By varying these factors in the framework, we implement eight algorithms and tested them on keyword based image search results from Google search engine. The experimental results confirm that how to select the training data from noisy search results is really a key issue in the problem considered in this paper and show that the proposed algorithm is effective to improve Google search results, especially at top ranks, thus is helpful to reduce the user labor in finding the desired images by browsing the ranking in depth. Even so, it is still worth meditative to make automatic training data selection scheme better towards perfect human annotation.
Energy Technology Data Exchange (ETDEWEB)
Backlund, Peter B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Shahan, David W. [HRL Labs., LLC, Malibu, CA (United States); Seepersad, Carolyn Conner [Univ. of Texas, Austin, TX (United States)
2014-04-22
A classifier-guided sampling (CGS) method is introduced for solving engineering design optimization problems with discrete and/or continuous variables and continuous and/or discontinuous responses. The method merges concepts from metamodel-guided sampling and population-based optimization algorithms. The CGS method uses a Bayesian network classifier for predicting the performance of new designs based on a set of known observations or training points. Unlike most metamodeling techniques, however, the classifier assigns a categorical class label to a new design, rather than predicting the resulting response in continuous space, and thereby accommodates nondifferentiable and discontinuous functions of discrete or categorical variables. The CGS method uses these classifiers to guide a population-based sampling process towards combinations of discrete and/or continuous variable values with a high probability of yielding preferred performance. Accordingly, the CGS method is appropriate for discrete/discontinuous design problems that are ill-suited for conventional metamodeling techniques and too computationally expensive to be solved by population-based algorithms alone. In addition, the rates of convergence and computational properties of the CGS method are investigated when applied to a set of discrete variable optimization problems. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, when compared to genetic algorithms.
Compiling Relational Bayesian Networks for Exact Inference
DEFF Research Database (Denmark)
Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark
2006-01-01
We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference...... by evaluating and differentiating these circuits in time linear in their size. We report on experimental results showing successful compilation and efficient inference on relational Bayesian networks, whose PRIMULA--generated propositional instances have thousands of variables, and whose jointrees have clusters...
Bayesian models a statistical primer for ecologists
Hobbs, N Thompson
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili
DEFF Research Database (Denmark)
Michael, OG; Kirk, O; Mathiesen, Lars Reinhardt
2002-01-01
Current antiretroviral therapy can induce considerable, sustained viral suppression followed by immunological recovery, in which naive CD4 + cells are important. Long-term immunological recovery was investigated during the first 3 y of highly active antiretroviral therapy (HAART) in 210 HIV-1...... was sustained. There was no association between plasma viral load and the increase in naive CD4 + cell count. Importantly, baseline naive CD4 + cell count was significantly associated with the change in naive CD4 + cell count, suggesting that the naive cell count at baseline does influence the immunological...
Design and evaluation of neural classifiers
DEFF Research Database (Denmark)
Hintz-Madsen, Mads; Pedersen, Morten With; Hansen, Lars Kai;
1996-01-01
In this paper we propose a method for the design of feedforward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme we derive a modified form of the entropy error measure and an algebraic estimate of the test error. In conjunction...
Large margin classifier-based ensemble tracking
Wang, Yuru; Liu, Qiaoyuan; Yin, Minghao; Wang, ShengSheng
2016-07-01
In recent years, many studies consider visual tracking as a two-class classification problem. The key problem is to construct a classifier with sufficient accuracy in distinguishing the target from its background and sufficient generalize ability in handling new frames. However, the variable tracking conditions challenges the existing methods. The difficulty mainly comes from the confused boundary between the foreground and background. This paper handles this difficulty by generalizing the classifier's learning step. By introducing the distribution data of samples, the classifier learns more essential characteristics in discriminating the two classes. Specifically, the samples are represented in a multiscale visual model. For features with different scales, several large margin distribution machine (LDMs) with adaptive kernels are combined in a Baysian way as a strong classifier. Where, in order to improve the accuracy and generalization ability, not only the margin distance but also the sample distribution is optimized in the learning step. Comprehensive experiments are performed on several challenging video sequences, through parameter analysis and field comparison, the proposed LDM combined ensemble tracker is demonstrated to perform with sufficient accuracy and generalize ability in handling various typical tracking difficulties.
Neural Classifier Construction using Regularization, Pruning
DEFF Research Database (Denmark)
Hintz-Madsen, Mads; Hansen, Lars Kai; Larsen, Jan;
1998-01-01
In this paper we propose a method for construction of feed-forward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme, we derive a modified form of the entropic error measure and an algebraic estimate of the test error. In conjunction...
Adaptively robust filtering with classified adaptive factors
Institute of Scientific and Technical Information of China (English)
CUI Xianqiang; YANG Yuanxi
2006-01-01
The key problems in applying the adaptively robust filtering to navigation are to establish an equivalent weight matrix for the measurements and a suitable adaptive factor for balancing the contributions of the measurements and the predicted state information to the state parameter estimates. In this paper, an adaptively robust filtering with classified adaptive factors was proposed, based on the principles of the adaptively robust filtering and bi-factor robust estimation for correlated observations. According to the constant velocity model of Kalman filtering, the state parameter vector was divided into two groups, namely position and velocity. The estimator of the adaptively robust filtering with classified adaptive factors was derived, and the calculation expressions of the classified adaptive factors were presented. Test results show that the adaptively robust filtering with classified adaptive factors is not only robust in controlling the measurement outliers and the kinematic state disturbing but also reasonable in balancing the contributions of the predicted position and velocity, respectively, and its filtering accuracy is superior to the adaptively robust filter with single adaptive factor based on the discrepancy of the predicted position or the predicted velocity.
Classifying Finitely Generated Indecomposable RA Loops
Cornelissen, Mariana
2012-01-01
In 1995, E. Jespers, G. Leal and C. Polcino Milies classified all finite ring alternative loops (RA loops for short) which are not direct products of proper subloops. In this paper we extend this result to finitely generated RA loops and provide an explicit description of all such loops.
Visual Classifier Training for Text Document Retrieval.
Heimerl, F; Koch, S; Bosch, H; Ertl, T
2012-12-01
Performing exhaustive searches over a large number of text documents can be tedious, since it is very hard to formulate search queries or define filter criteria that capture an analyst's information need adequately. Classification through machine learning has the potential to improve search and filter tasks encompassing either complex or very specific information needs, individually. Unfortunately, analysts who are knowledgeable in their field are typically not machine learning specialists. Most classification methods, however, require a certain expertise regarding their parametrization to achieve good results. Supervised machine learning algorithms, in contrast, rely on labeled data, which can be provided by analysts. However, the effort for labeling can be very high, which shifts the problem from composing complex queries or defining accurate filters to another laborious task, in addition to the need for judging the trained classifier's quality. We therefore compare three approaches for interactive classifier training in a user study. All of the approaches are potential candidates for the integration into a larger retrieval system. They incorporate active learning to various degrees in order to reduce the labeling effort as well as to increase effectiveness. Two of them encompass interactive visualization for letting users explore the status of the classifier in context of the labeled documents, as well as for judging the quality of the classifier in iterative feedback loops. We see our work as a step towards introducing user controlled classification methods in addition to text search and filtering for increasing recall in analytics scenarios involving large corpora.
Classifying web pages with visual features
de Boer, V.; van Someren, M.; Lupascu, T.; Filipe, J.; Cordeiro, J.
2010-01-01
To automatically classify and process web pages, current systems use the textual content of those pages, including both the displayed content and the underlying (HTML) code. However, a very important feature of a web page is its visual appearance. In this paper, we show that using generic visual fea
Effects of a calm companion on fear reactions in naive test horses
DEFF Research Database (Denmark)
Christensen, Janne Winther; Malmkvist, Jens; Nielsen, Birte Lindstrøm
2008-01-01
Reasons for performing study: In fear-eliciting situations, horses tend to show flight reactions that can be dangerous for both horse and man. Finding appropriate methods for reducing fearfulness in horses has important practical implications. Objectives: To investigate whether the presence...... of a calm companion horse influences fear reactions in naive subject horses. Hypotheses: The presence of a habituated (calm) companion horse in a fear-eliciting situation can reduce fear reactions in naive subject horses, compared to subject horses with a nonhabituated companion (control). Methods......: Minimally handled (n = 36), 2-year-old stallions were used, 18 as subjects and 18 as companions. Companion horses (n = 9) were habituated to an otherwise frightening, standardised test stimulus (calm companions), whereas the rest (n = 9) of the companion horses remained nonhabituated (control companions...