Full Bayesian Inference for Double Penalty Models
Bayesian Inference for Double Seasonal Moving Average Models: A Gibbs Sampling Approach
... (2013). Bayesian analysis of SARMA model for single seasonality has been well established, and different approaches have been developed in ...the Bayesian time series ...achieve Bayesian analysis for ...
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Semiparametric Bayesian inference in smooth coefficient models
... parametric models when spousal income is set at $120,000 (approximately the 95th percentile of the spousal income distribution), and thus investigate the impact of ability on female labor supply decisions for ...
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Semiparametric Bayesian inference in multiple equation models
... 2.1 The Parametric SEM In this section we show how our techniques can be applied in practice. Our empirical example, though primarily illustrative in nature, simultaneously addresses several topics of considerable ...
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Semiparametric Bayesian inference in multiple equation models
... 2.1 The Parametric SEM In this section we provide an empirical example to illustrate how our techniques can be applied in practice. Our specific example, though primarily illustrative in nature, simultaneously addresses ...
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Bayesian Inference of Multiple Gaussian Graphical Models
... no penalty placed on the difference across groups, the estimated adjacency matrices share a substantial number of ...a full ROC curve for these methods. The ROC curves for the Bayesian methods are ...
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Accelerating Bayesian inference for evolutionary biology models.
... both Full MrBayes and ...of Full MrBayes using each four tempered chains with four separate runs of our best method, PCA, with 4 tempered chains having each 32 proces- sors dedicated to our coupling of ...
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Semiparametric Bayesian inference in multiple equation models
... 2.1 The Parametric SEM In this section we provide an empirical example to illustrate how our techniques can be applied in practice. Our specific example, though primarily illustrative in nature, simultaneously addresses ...
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Bayesian Inference in Spatial Sample Selection Models
... the Bayesian estimator in all algorithms reports estimates that are close to the true parameter value for the autoregressive parameter of the selection ...the Bayesian estimator in Algorithms ...the ...
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On efficient Bayesian inference for models with stochastic volatility
... their full conditional distribution for each i = 1, ...BVAR models, es- timates based on the latter algorithm seemed to be unduly dependent on the priors and prone to yielding highly variable estimates of ...
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Bayesian Inference for Exponential Random Graph Models
... Figure 10: Molecule synthetic graph. We use the flat prior π(θ i ) ∼ N (0, 30), for i = 1, . . . , 4, and we set γ = 0.5 and ∼ N (0, 0.1I) corresponding to an overall acceptance probability of 22%. The auxiliary chain ...
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Bayesian Inference in Spatial Sample Selection Models
... the Bayesian estimator in all algorithms reports estimates that are close to the true parameter value for the autoregressive parameter of the selection ...the Bayesian estimator in Algorithms ...the ...
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Efficient Bayesian inference for COM-Poisson regression models
... three Bayesian COM-Poisson regression models; each one with a vague multivariate normal prior on β and a different prior for δ ...two models use a shrinkage prior (lasso or spike and slab) for ...a ...
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Bayesian inference for double Pareto lognormal queues
... developed Bayesian inference for the double Pareto log- normal distribution and have illustrated that this model can capture both the heavy tail behavior and also the body of the distribution for ...
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Bayesian Inference in Nonparanormal Graphical Models.
... a Bayesian method using a frequentist yardstick in the large sample setting, and is of interest to both frequentists and Bayesians; for a thorough account of posterior consistency, see Ghosal & Vaart ...graphical ...
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Bayesian inference on mixture models and their applications
... Mixture models are useful in describing a wide variety of random phenomena because of their flexibility in ...mixture models, we introduce a skew-normal mixture model and adapt the reversible jump Markov ...
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Bayesian inference of fisheries and ecology models
... 95(9):2548-2557. Peterson, I., and J.S. Wroblewski. 1984. Mortality rate of fishes in the pelagic ecosystem. Canadian Journal of Fisheries and Aquatic Sciences 41:1117-1120. Pfaller, J.B., K.A. Bjorndal, M. Chaloupka, ...
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Optimal inference with suboptimal models: addiction and active Bayesian inference.
... erative models that ‘suboptimally’ approximate the true causal structure of the ...in Bayesian inference, as studied in machine learning ...data, models have to optimise the trade-off between ...
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Optimal inference with suboptimal models: addiction and active Bayesian inference
... erative models that ‘suboptimally’ approximate the true causal structure of the ...in Bayesian inference, as studied in machine learning [11,56] ...data, models have to optimise the trade-off ...
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Bayesian Inference for Sparse Generalized Linear Models
... approximate Bayesian inference in generalized linear models (GLMs), based on the ex- pectation propagation (EP) ...in Bayesian GLMs is emphasized and related to stability issues in ...
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Bayesian Inference on Dynamic Models with Latent Factors
... A Bayesian approach can sometimes be preferable since it permits to treat general state space models and makes easier the simulation based approach to parameters estimation and latent factors ...series ...
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