Bayesian approach
A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning
... a Bayesian method for model selection was proposed by Seeger [19], where a maximum a posteriori (MAP) criterion on the parameters is imposed using a vari- ational ...embedded approach for the quadratic ...
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Bayesian analysis of polarization measurements
... integrating the posterior distribution over the subset. If the prob- ability is greater than 50%, the hypothesis is accepted. If it is less than 50%, it is rejected. The simplicity of these tests is one of the benefits ...
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Scaling relations of the colour detected cluster RzCS 052 at z=1 016 and some other high redshift clusters
... a Bayesian approach to measuring cluster velocity dispersions and X-ray luminosities in the presence of a background: we critically reanalyse recent claims for X-ray underluminous clusters using these ...
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Bayesian Reliability, Availability and Maintainability Analysis for Hardware Systems Described Through Continuous Time Markov Chains
... standard approach to RAM estimation of CTMC HW systems com- putes Maximum Likelihood Estimates (MLEs) for the involved parameters of the CTMC, substitutes parameters by the MLEs, computes the equilibrium dis- ...
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Bayesian statistics in genetics
... cases, Bayesian methods can address the question of interest more directly than a classical ...A Bayesian approach can reflect a more relevant question, which might be ‘are departures from HWE large ...
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Environment, mathematics and the best solution to stop natural world destruction
... use Bayesian statistics in a rather primitive way, changing prior ideas after observations, therefore, additional assumptions should be made to obtain solid ...modern Bayesian approach ought to lead ...
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Phylogeography Takes a Relaxed Random Walk in Continuous Space and Time
... a Bayesian implemen- tation. In particular, a Bayesian approach permits the easy integration of different sources of uncertainty and also af- fords more flexible incorporation of geographic ...
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Decision boundary for discrete Bayesian network classifiers
... different Bayesian networks considered are not in general ...given Bayesian network classifier, and we will be able to compute the gain in expressivity from simple to more complicated Bayesian ...
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TítuloRobust Precoding with Bayesian Error Modeling for Limited Feedback MU MISO Systems
... Most of the work on precoding with erroneous CSI was motivated by a Time Division Duplex (TDD) setup, where the transmitter can estimate the CSI during the transmission in the opposite direction [13], [14]. This ...
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Learning Tractable Bayesian Networks
... Scanagatta et al. [2016] proposed a method (called k-greedy) for learning bounded tree- width BNs from very large datasets. Before performing the structure search, k-greedy initial- izes a cache of candidate parent sets ...
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Prediction in health domain using Bayesian networks optimization based on induction learning techniques
... The Bayesian networks can make the classification task, a particular case of prediction, that it is characterized to have a single variable of the database (class) that we desire to predict, whereas all the others ...
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Pronóstico de grandes sismos mediante el análisis de semiperiodicidad de procesos puntuales etiquetadosForecast of large earthquakes through semi-periodicity analysis of labeled point processes
... Uses Bayesian analysis to evaluate aftcast (forecast done a posteriori) ...through Bayesian analysis and compared with the updated forecast probability for the sequence including the last ...
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Bayesian analysis of textual data
... tree approach correctly classifies D1 to be by Author 1 in 639 out of the 1000 realizations, the support vector machine approach does that 588 times and the logistic regression ap- proach does that 653 ...
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The evolution of ecomorphological traits within the Abrothrichini (Rodentia : Sigmodontinae): A Bayesian phylogenetics approach
... j . We estimated the aspects of trait evolution using a Bayesian MCMC framework (Pagel et al., 2004), selecting the parameter val- ues from the chains that gave the highest value of likelihood given the model of ...
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A. J. Drummond, A. Rambaut, B. Shapiro, and O. G. Pybus - Bayesian skyline plots
... the Bayesian skyline plot model, we analyzed two simulated data sets using our MCMC ...the Bayesian sky- line plot and the ancestral ...a Bayesian skyline plot, shown in figure ...
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Detection and tracking of multiple targets using wireless sensor networks - Detección y seguimiento de múltiples blancos en redes inalámbricas de sensores
... properly and estimate the target states. Conventional particle filters with finite number of particles fail to do the former and estimators based on the MMSE fail to do the latter. This section explains why this ...
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Contributions to Bayesian network learning with applications to neuroscience
... In real life problems, continuous data may not fit any standard parametric distribution. Therefore, the assumption of a parametric shape might yield misleading conclusions or re- sults. Non-parametric density estimation ...
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Prediction of Federal Funds Target Rate: a dynamic logistic Bayesian Model averaging approach
... Table 5 lists the complete set of 7 potential predictors we use in our analysis. It is worth mentioning that variables measured at a quarterly frequency, such as Expected y/y GDP (data gathered from Bloomberg’s survey), ...
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Robust techniques for multiple target tracking and fully adaptive radar = Técnicas robustas para seguimiento de múltiples blancos y radar adaptativo
... approximated Bayesian filtering techniques severely ...robust Bayesian filtering methods which can accurately provide estimates in high-dimensional state ...
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The design of a Bayesian Network for mobility management in Wireless Sensor Networks
... a Bayesian network (BN) approach for making explicit the structural and parametric components of a movement context using WSN ...BN approach provides several advantages regarding to the probabilistic ...
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