A Gaussian copula with marginal Bayesian splines
Pseudo-marginal Bayesian inference for Gaussian Processes
... Pseudo-Marginal Bayesian Inference for Gaussian Processes Maurizio Filippone and Mark Girolami Abstract—The main challenges that arise when adopting Gaussian Process priors in probabilistic ...
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Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models
... efficient Bayesian framework to discover potential risk factors and investigate which fingers are jointly ...Our Bayesian approach is based on Gaussian copula graphical models, which provide a ...
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Copula Gaussian graphical modelling of biological networks and Bayesian inference of model parameters
... the Gaussian copula graphical model is performed as it enables one to partition a high-dimensional joint dis- tribution function as pieces of marginal that are bound by a separate copula term ...
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Copula Bayesian Networks
... linear Gaussian BN were almost identical to those of the sigmoid BN for the Wine and Dow Jones datasets and inferior for the Crime dataset, and are omitted for ...the copula based models offer a clear gain ...
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Effects of marginal specifications on copula estimation
... a copula density or probability mass func- tion for estimation ...purposes. Bayesian methods can in this case provide a possible ...a Bayesian sampling scheme for discrete and continuous margins in a ...
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A Bayesian Approach to Inference and Prediction for Spatially Correlated Count Data Based on Gaussian Copula Model
... our Bayesian approach gener- ates more robust results than the MML method, and produces better predictions than GAM method, especially when the missing count values are small (close to ...the Bayesian ...
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Application of Parametric and Nonparametric Copula Marginal Models in Recurrent Failure Times of Childhood Seizures: Bayesian Approach
... all marginal failure times with shape parameter ρ>0 and scale parameter β ° where β ° ∈ R ...the copula parameter tends to infinity, the dependence becomes maximal and as copula parameter tends to ...
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Extension of Spot Recovery Model for Gaussian Copula
... It should be emphasized that this example is for illustration purpose only. The recovery distribution used is not realistic, but is close to the recovery markdown to zero. It has maximum variance, which should help with ...
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Bayesian Nonparametric Inference for a Multivariate Copula Function
... each marginal distribution F m of H is continuous, then C is ...Parametric copula models have been extensively ...the Gaussian copula and the Student t copula; and the Clayton ...
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Copula selection models for non-Gaussian responses that are missing not at random
... flexible copula-based selection model, which can help make more plau- sible assumptions about the distribution of the data and offer flexibility about the choice of joint model for both the outcome and ...
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Eliciting Dirichlet and Gaussian copula prior distributions for multinomial models
... a Gaussian copula function with beta marginals to model the joint prior distribution of multinomial proba- ...proposed Gaussian copula prior assumes that the dependence structure between the ...
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Multi-task Sparse Structure Learning with Gaussian Copula Models
... in Gaussian graphical models endowed with sparse estimators of the precision (inverse covariance) ...flexible Gaussian copula models that relaxes the Gaussian marginal assumption is ...
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GPstuff: Bayesian Modeling with Gaussian Processes
... a Gaussian observation model which enables an analytic solution for the marginal likelihood p(y|X, ϑ) and the conditional posterior p(f|X, ...the marginal likelihood and the conditional posterior ...
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Perturbing the structure in Gaussian Bayesian networks
... Sensitivity analysis is a general technique to evaluate the effects of inaccuracies in the parameters of the model on the model’s output. In BNs, the network’s output, given by the marginal distribution of inter- ...
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Accelerating pseudo marginal MCMC using Gaussian processes
... perform Bayesian inference in la- tent variable ...a Gaussian process (GP) approximation to the log-likelihood and train this GP using a short pilot run of the MCWM ...
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Accelerating pseudo-marginal MCMC using Gaussian processes
... perform Bayesian inference in latent variable ...use Gaussian processes (GP) to accelerate the GIMH method, whilst using a short pilot run of MCWM to train the ...
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Improving posterior marginal approximations in latent Gaussian models
... Expectation propagation can still be applied when the Laplace approximation is doomed to fail. An example is Bayesian linear regression with a double-exponential prior (Seeger, 2008). Direct application of the ...
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Copula models for dependence: comparing classical and bayesian approaches
... 5 | Comments, Conclusions and Future Work The main purpose of this work was to model the dependence between real wind speed data and simulated wind speed data using bivariate copulas. The major benefit of using the ...
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Bayesian P-splines with a multiplicative term in EMG trace data
... 6 Bayesian P-splines with a multiplicative term in EMG trace data investigations involving ...the marginal likelihood or statistical evidence, though such a presentation is beyond the scope of this ...
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Meta-model of a large credit risk portfolio in the Gaussian copula model
... = d N (0, 1) is an idiosyncratic risk associated with the kth obligor (independent from Z ), ρ k ∈ (−1, 1) a correlation parameter. The (ε k ) 1≤k≤K are independent, and independent from Z . The extension to a ...
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