- 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
Approximating Max‐Sum‐Product Problems using Multiplicative Error Bounds
Approximating Max‐Sum‐Product Problems using Multiplicative Error Bounds
- Chapter:
- (p.439) Approximating Max‐Sum‐Product Problems using Multiplicative Error Bounds
- Source:
- Bayesian Statistics 9
- Author(s):
Christopher Meek
Ydo Wexler
- Publisher:
- Oxford University Press
We describe the Multiplicative Approximation Scheme (MAS) for approximate inference in multiplicative models. We apply this scheme to develop the DynaDecomp approximation algorithm. This algorithm can be used to obtain bounded approximations for various types of max‐sum‐product problems including the computation of the log probability of evidence, the log‐partition function, Most Probable Explanation (MPE) and maximum a posteriori probability (MAP) inference problems. We demonstrate that this algorithm yields bounded approximations superior to existing methods using a variety of large graphical models.
Keywords: Approximate inference, graphical models, max‐sum‐product, sum‐product
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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
