Bayesian model-based approaches
Bayesian model based approaches in the analysis of chromatin structure and motif discovery
... a model based classification approach in several areas of biomedical research ...the model, once established, would help us in using the distributional properties of the normal distribution in ...
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Learning Bayesian networks based on optimization approaches
... This thesis focuses on BN models; in particular structure learning and parameter learning. We find structures and parameters in BNs by introducing different strategies. Since the definition of structures in BNs is a very ...
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Parameter Estimation of Cobb Douglas Production Function with Multiplicative and Additive Errors using the Frequentist and Bayesian Approaches
... introduced Bayesian approach to nonlinear model and confirmed by Royall ([Roy97]) that a true likelihood approach is difficult to calibrate since all approaches based on classical criteria ...
7
Estimating proportional hazards model using frequentist and bayesian approaches
... analysis. Bayesian models can be compared using the deviance information criterion (DIC), which its posterior distributions have been obtained using ...package. Bayesian inference has several advantages ...
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Inferences for Burr-X Model Based on Unified Hybrid Censored Data
... the Bayesian and the E-Bayesian approaches, for estimating the unknown parameter and the reliability function of Burr-X distribution based on ...The Bayesian and E- Bayesian ...
7
Analysis of Bayesian classification-based approaches for Android malware detection
... For the Android platform, a paper by Sahs and Khan [21] presented a machine learning approach for Android malware detection based on SVM. A single-class SVM model derived from benign samples alone is used. ...
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Bayesian Approaches For Modeling Variation
... Nonlinear factor analysis may be viewed as a nonlinear dimensionality reduction method that has a specific likelihood, given in (4.2). Our framework confers a number of benefits over traditional methods for nonlinear ...
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Validating and updating a risk model for pneumonia – a case study
... prediction model for the presence of pneumonia. The genuine pre- diction model was based on expert opinion rather than on an epidemiologic study with real patient ...expert model to be of very ...
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A Comparison of Frequentist and Bayesian Approaches: The Power to Detect Model Misspecifications in Confirmatory Factor Analytic Models
... CFA based on BSEM approach along with EFA to examine the factor structure of the educational and psychological ...CFA model affect the results in BSEM approach, one should be elaborately investigate related ...
21
Model averaging in ecology: : a review of Bayesian, information-theoretic and tactical approaches for predictive inference
... non-parametric approaches to compute model weights: stacking and jackknife model averaging (see Appendix S4 for discussion of averaging within machine-learning ...cross-validation based, and ...
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Bayesian and Non Bayesian Estimation of the Inverse Weibull Model Based on Generalized Order Statistics
... inference based on n selected generalized order statistics (GOS) from inverse Weibull distribution (IWD), Bayesian and non-Bayesian approaches have been used to obtain the estimators of the ...
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Gene Expression-Based Glioma Classification Using Hierarchical Bayesian Vector Machines
... proposed Bayesian probit model approaches with latent variables for modelling cancer tumours with more than two ...linear model to model the latent variables. In contrast, our ...
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Probabilistic machine learning and artificial intelligence
... nonparametric model for unknown functions, and are widely used for regression, classification, and many other applications that require inference on functions ...nonparametric model with a long history in ...
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Comparison of Bayesian and frequentist group sequential clinical trial designs
... We have considered stopping for a positive result only. In practice, with both frequentist and Bayesian group- sequential designs, it is often desirable to allow stopping when a lack of efficacy is clear, that is ...
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Estimation of the Piecewise Exponential Model by Bayesian P Splines via Gibbs Sampling: Robustness and Reliability of Posterior Estimates
... sian, based on the relationship between penalized splines and mixed models ...by Bayesian P-splines. A further facilitation is that widespread Bayesian software, such as WinBUGS, can be ...PE ...
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Analysis of Probabilistic Parsing in NLP
... symbolic approaches is seen in logic or rule-based systems. In logic based systems, the symbolic structure is usually in the form of logic ...symbolic approaches is semantic networks. First ...
11
Probabilistic classification of quality of service in wireless computer networks
... Figures 8 a-c show the atypicality index (obtained using Equation 9) for the Bayesian classifier for the packets in the test file associated with paths BC-1, BC-2 and BC-3 respectively. These also correspond to ...
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Adaptive Bayesian SLOPE—High-dimensional Model Selection with Missing Values
... Handling missing data within the context of high-dimensional variable selection is a very important problem. Indeed, missing data are omnipresent. For example, genetic data obtained from microarray experiments often ...
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Topology, divergence dates, and macroevolutionary inferences vary between different tip-dating approaches applied to fossil theropods (Dinosauria)
... (c) 2016 The Author(s). Published by the Royal Society. All rights reserved. This is an author produced version of "Bapst, DW, Wright, AM, Matzke, NJ & Lloyd, GT (2016) Topology, divergence dates, and ...
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Model averaging in ecology: a review of Bayesian, information-theoretic and tactical approaches for predictive inference
... In fishery science, averaging process models is relatively common (Brodziak and Piner, 2010), as it is in weather and climate science (Krishnamurti et al., 1999; Knutti et al., 2010; Bauer et al., 2015). There are at ...
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