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- Title Pages
- Preface
- Integrated Objective Bayesian Estimation and Hypothesis Testing
- Dynamic Stock Selection Strategies: A Structured Factor Model Framework*
- Free Energy Sequential Monte Carlo, Application to Mixture Modelling*
- Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs*
- Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels
- Bayesian Variable Selection for Random Intercept Modeling of Gaussian and Non‐Gaussian Data
- External Bayesian Analysis for Computer Simulators*
- Optimization Under Unknown Constraints*
- Using TPA for Bayesian Inference*
- Nonparametric Bayesian Networks*
- Particle Learning for Sequential Bayesian Computation*
- Rotating Stars and Revolving Planets: Bayesian Exploration of the Pulsating Sky*
- Association Tests that Accommodate Genotyping Uncertainty*
- Bayesian Methods in Pharmacovigilance*
- Approximating Max‐Sum‐Product Problems using Multiplicative Error Bounds
- What's the H in H‐likelihood: A Holy Grail or an Achilles' Heel?*
- Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction*
- Bayesian Models for Sparse Regression Analysis of High Dimensional Data*
- Transparent Parametrizations of Models for Potential Outcomes
- Modelling Multivariate Counts Varying Continuously in Space*
- Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models*
- Bayesian Models for Variable Selection that Incorporate Biological Information*
- Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology
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- Title Pages
- Preface
- Integrated Objective Bayesian Estimation and Hypothesis Testing
- Dynamic Stock Selection Strategies: A Structured Factor Model Framework*
- Free Energy Sequential Monte Carlo, Application to Mixture Modelling*
- Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs*
- Nonparametric Bayes Regression and Classification Through Mixtures of Product Kernels
- Bayesian Variable Selection for Random Intercept Modeling of Gaussian and Non‐Gaussian Data
- External Bayesian Analysis for Computer Simulators*
- Optimization Under Unknown Constraints*
- Using TPA for Bayesian Inference*
- Nonparametric Bayesian Networks*
- Particle Learning for Sequential Bayesian Computation*
- Rotating Stars and Revolving Planets: Bayesian Exploration of the Pulsating Sky*
- Association Tests that Accommodate Genotyping Uncertainty*
- Bayesian Methods in Pharmacovigilance*
- Approximating Max‐Sum‐Product Problems using Multiplicative Error Bounds
- What's the H in H‐likelihood: A Holy Grail or an Achilles' Heel?*
- Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction*
- Bayesian Models for Sparse Regression Analysis of High Dimensional Data*
- Transparent Parametrizations of Models for Potential Outcomes
- Modelling Multivariate Counts Varying Continuously in Space*
- Characterizing Uncertainty of Future Climate Change Projections using Hierarchical Bayesian Models*
- Bayesian Models for Variable Selection that Incorporate Biological Information*
- Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology
