Conservative Bayesian inference with partial priors
Bayesian Inference for Spatio-temporal Spike-and-Slab Priors
... We investigated the role of the spatio-temporal prior and the approximation schemes in a series of experiments. First we studied a simple 1D problem with spatial, translational invariant smoothness of the support (single ...
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On Bayesian inference with conjugate priors for scale mixtures of normal distributions
... Abstract Bayesian inference is considered for the multivariate regression model with distribu- tion of the random responses belonging to the multivariate scale mixtures of normal ...gives inference ...
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Benchmark Priors for Bayesian Model Averaging
... recent Bayesian literature in this area, we consider a prior distribution that allows for the actual exclusion of regressors from some of the models —see ...continuous inference about the full vector β ...
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A Comment on Priors for Bayesian Occupancy Models
... these priors can impact inference on the habitat factors influ- encing ...that Bayesian analyses pro- vide full posterior distributions for the probability of a parameter conditional on the data, and ...
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Application of Gaussian Process Priors on Bayesian Regression
... However, there is very limited literature on either nonparametric or semiparametric models for missing covariates data. One common approach for nonparametric model- ing is splines, such as using basis function ...
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Bayesian Mixture Models with Weight-Dependent Component Priors for Bayesian Clustering
... chical priors and derive the corresponding posteriors. In the Bayesian inference for Gaussian mixtures, it is common to choose the component parameter priors to be independent of ...for ...
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Partial inversion of the elliptic operator to speed up computation of likelihood in Bayesian inference
... the Bayesian framework, the state of knowledge is modeled in a probabilistic ...The Bayesian setting allows updating/sharpening of information about q when the measurement is ...
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Constrained parameter estimation with uncertain priors for Bayesian networks
... in Bayesian statistical inference is to choose a class Γ of prior distributions and compute some quantity, such as the posterior risk, the Bayes risk or the posterior expected value, as the prior ranges ...
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Bayesian nonparametric system reliability using sets of priors
... imprecise Bayesian nonparametric approach to system reliability with multiple types of components is ...modelling partial or imperfect prior knowledge on component failure distributions in a flexible way ...
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Priors in Whole-Genome Regression: The Bayesian Alphabet Returns
... for inference that is uncontaminated from the effects of the prior, except in a ...subspace. Bayesian methods for confront- ing the blatant overparameterization of genomic selection models are reviewed in ...
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Bayesian nonparametric system reliability using sets of priors.
... imprecise Bayesian nonparamet- ric approach to system reliability with multiple types of ...modelling partial or imperfect prior knowledge on compo- nent failure distributions in a flexible way through ...
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Bayesian fmri time series analysis with spatial priors
... a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models ...and inference in GLMs using Posterior Probability Maps ...
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Bayesian analysis of ARMA models using noninformative priors
... on Bayesian analysis of the posteriors of locally nonidenti¯ed parameters is still quite small though, see ...di®use priors are used. To overcome this implicit favor when using di®use priors, in [11] ...
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Polytomies and bayesian phylogenetic inference
... in Bayesian MCMC analyses allows less-resolved tree topologies containing one or more polytomies to compete with fully-resolved tree topolo- gies for posterior ...a conservative approach that reduces ...
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Bayesian inference via projections
... informative priors for more complicated models might be harder to encode in the Bayesian projections for- mulation (although the projection algorithm itself can be defined to incorporate prior ...of ...
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Bayesian inference via projections
... informative priors for more complicated models might be harder to encode in the Bayesian projections for- mulation (although the projection algorithm itself can be defined to incorporate prior ...of ...
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Priors in Bayesian Learning of Phonological Rules
... Our point in this paper, however, is not to present a fully general learner, but to emphasize that in a Bayesian system, the choice of prior can be crucial to the success of the learning task. Learning is a ...
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Benchmark priors for Bayesian models averaging
... We focus on the Normal linear regression model with uncertainty in the choice of re- gressors. We propose a partly noninformative prior structure related to a Natural Conjugate g-prior specification, where the amount of ...
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Priors on the Variance in Sparse Bayesian Learning; the demi-Bayesian Lasso
... Laplace prior, and conducts fully Bayesian inference (via Markov chain Monte Carlo or MCMC sampling algorithms) for parameter inference. A number of recent papers have explored connections between ...
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Bayesian statistical inference
... (i.e. Bayesian) within the area of decision theory, considered as a distinct element of the chain of considerations forming, as a whole, the rational procedure to be followed in order to choose a decision in the ...
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