sequential Bayesian model selection
Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo
... the model posterior proba- bilities over the different populations and the associated tolerance ...kernel model is more ...simple model, which is the Model Two. This means that the complex ...
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Sequential variable selection as Bayesian pragmatism in linear factor models
... We choose two different sample periods, where SMB and HML are either positively or negatively correlated. Table 2A lists the regression results for the period from 1935 Jan to 1954 Dec, where SMB and HML are positively ...
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Bayesian model selection for the glacial interglacial cycle
... fully Bayesian approach that simultaneously estimates model param- eters, the relative contribution of each aspect of the orbital forcing, and chooses between models by estimating Bayes ...a ...
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Bayesian model comparison via sequential Monte Carlo
... In settings in which the importance weights at time 𝑡 depend only upon the sam- ples at time 𝑡 − 1, such as that considered here, it is relatively straightforward to consider sample-dependent, adaptive specification of ...
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Model selection within a Bayesian approach to extraction of walker motion
... Capturing motion of walking people is an important task in the domain of computer vision, and many systems have been developed to solve this problem [2, 3, 4]. In 1983, Hogg [5] carried out pioneering work using an ...
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A Bayesian decision-theoretic model of sequential experimentation with delayed response
... Bayes Sequential policy to the left of D and to the right of C, so that there is no difference between the expected ...Bayes Sequential policy benefits from the arrival of observations on the pipeline ...
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Sequential Variable Selection as Bayesian Pragmatism in Linear Factor Models
... We choose two different sample periods, where SMB and HML are either positively or negatively correlated. Table 6 lists the regression results for the period from 1935 Jan to 1954 Dec, where SMB and HML are posi- tively ...
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Gene Expression-Based Glioma Classification Using Hierarchical Bayesian Vector Machines
... the model. As we have pointed out already that the classification of Glioma cancer is extremely difficult, we observe that most of the standard methods like neural network and random forest do equally poorly in ...
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Incorporating cost in Bayesian Variable Selection, with application to cost-effective measurement of quality of health care.
... • Patient sickness at admission is traditionally assessed by using logistic regression of mortality within 30 days of admission on a fairly large number of sickness indicators (on the order of 100) to construct a ...
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Bayesian Variable Selection of Risk Factors in the APT Model
... Variable selection adds an extra source of uncertainty to the model and an extra dimension to the complexity of the first method and to the unreliability of the inference in the second ...APT model ...
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Priors for Bayesian Shrinkage and High-Dimensional Model Selection
... the Bayesian Lasso (Park and Casella, 2008; Hans, 2009), the horseshoe priors (Carvalho et ...between Bayesian model averaging and shrinkage, refer to Polson and Scott ...
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Adaptive surrogate modeling for response surface approximations with application to bayesian inference
... using Bayesian inference is usually a very costly process as it requires a large number of solves of the forward ...reduced model with respect to the observables utilized in the identification of the ...
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Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models
... The model with the greatest posterior probability is the purely noncausal AR(0,3) ...same model as the model selection procedure of Lanne and Saikkonen ...noncausal model suggests that ...
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Back to Basics for Bayesian Model Building in Genomic Selection
... the Bayesian G-BLUP (correlation 0.63 with both Bayesian G-BLUP and the best of the association models, Table ...the Bayesian version of G-BLUP with simul- taneously estimated variance components was ...
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A Bayesian Model of Sample Selection with a Discrete Outcome Variable
... sample selection model with multiple dichoto- mous dependent variables is estimated by methods of classical ...their model in a simulated maximum likelihood ...a Bayesian econometric ...
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Bayesian MAP model selection of chain event graphs
... basic Bayesian network in order to create so-called “context-specific” Bayesian networks ...a model in a non-graphical way, thus undermining the rationale for using a graphical model in the ...
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Multi objective model selection algorithm for online sequential ultimate learning machine
... With the development of network information tech- nology, using network control to transmit data be- comes a necessary means for people to transmit data and exchange information. An online sequential ex- treme ...
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Adaptively weighted group Lasso for semiparametric quantile regression models
... High dimensional covariate issues have been important and intractable ones. How- ever, some useful procedures have been proposed, for example, the SCAD in [9], the Lasso in [28], and the group Lasso in [34] and [24]. The ...
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Bayesian Spectral Estimation Applied to Echo Signals from Nonlinear Ultrasound Scatterers
... parametric Bayesian spectral estimation method is utilised for the analysis of the backscattered echo signals from ...The Bayesian spectral analysis technique has improved frequency resolution compared with ...
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Model Uncertainty in Claims Reserving within Tweedie's Compound Poisson Models
... Poisson model with p ! (1, 2) closes the gap between the Poisson and the gamma ...study Model Uncertainty, that is, we would like to study the sensitivity of the claims reserves within this sub- family, ...
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