Global ETD Search
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Showing 1 to 12 of 12 for “"Mixture Distributions"”.
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Bayesian analysis of finite mixture distributions using the allocation sampler
Finite mixture distributions are receiving more and more attention from statisticians in many different fields of research because they are a very flexible class of models. They are typically used for density estimation or to model population heterogeneity. One can think of a finite mixture …
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Modelling breakdown durations in simulation models of engine assembly lines
… has been derived to group machines with similar distributions of breakdown durations, where the Two-Sample Cram´er-von Mises statistic and bootstrap resampling are used to measure the similarity of two sets of data. We use finite mixture distributions fitted to the breakdown durations data of …
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Creating Models Of Internet Background Traffic Suitable For Use In Evaluating Network Intrusion Detection Systems
… Due to the complexity of the traffic, hybrid distributions (called mixture distributions) were sometimes required. The traffic models are demonstrated in two environments: NS-2 (a simulator) and HONEST (a lab environment). The simulation results are compared against the original captured data …
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Modified half logistic - exponentiated Kumaraswamy distribution and applications
The statistical model of mixture distributions has become an important tool for analyzing complicated phenomena in the real world. This approach has a wide range of applications in the fields of biology, medicine, environment and engineering. Based on the characteristics of the Exponentiated …
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Predictive reliabilities for electronic components
… as random, the failure time is expressed as the mixture distribution obtained from the conditional first passage time distributions. The mixture distributions are well represented by a Weibull distribution. A computer program is developed to compute the parameters of the Weibull distribution …
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Bayesian Multilevel-multiclass Graphical Model
… mate the classes of the observations from the mixture distributions by evaluating the Bayes factor and learn the network structures by fitting a novel neighborhood selection algorithm. This approach is able to identify the class membership and to reveal network structures for multilevel …
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Bayesian Hidden Markov Models for finding DNA Copy Number Changes from SNP Genotyping Arrays
… underlying DNA copy number. Copy-number-specific distributions, including a non-symmetric distribution for the 0-copy state (homozygous deletions) and mixture distributions for 2-copy state (normal), are developed and shown to be more appropriate than existing implementations which lead to …
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Statistical process control by quantile approach.
… distributed, e.g. Weibull, logistic or mixture data is increasingly encountered. Any method that seeks to avoid the use of transformation for non-normal data requires techniques for identification of the appropriate distributions. In cases where the appropriate distributions are known it …
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Natural gradient methods in statistics and machine learning
… for optimising the parameters of probability distributions where a direct application of natural gradients would be computationally demanding. We apply this method to maximum likelihood estimation and variational inference tasks involving a number of distributions. These include: …
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Score Estimation for Generative Modeling
… by a novel approach for score estimation of mixture distributions. Our framework is simple to implement, stable during training, unifies several existing approaches, and achieves state-of-the-art performance in image generation tasks. Furthermore, we discuss how this framework can be …
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Random Effects Selection In Bayesian Accelerated Failure Time Model With Correlated Interval Censored Data
… AFT model has been specified using a Gaussian mixture to allow flexible error density and prediction of the survival and hazard functions. We demonstrate the model using extensive simulations and the Signal Tandmobiel Study®.</p>
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Aspects of generative and discriminative classifiers
… thresholds for a variety of histograms of mixture distributions. Results show that the discriminative Otsu method consistently provides relatively good performance. Although being of higher computational complexity than the original methods in parameter estimation, its robustness and model …