Bayesian coresets applied to network intrusion data reduce sample counts while supporting accurate posterior inference in both offline and streaming regimes.
Edward: A library for probabilistic modeling, inference, and criticism
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abstract
Probabilistic modeling is a powerful approach for analyzing empirical information. We describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative process pioneered by George Box: build a model of a phenomenon, make inferences about the model given data, and criticize the model's fit to the data. Edward supports a broad class of probabilistic models, efficient algorithms for inference, and many techniques for model criticism. The library builds on top of TensorFlow to support distributed training and hardware such as GPUs. Edward enables the development of complex probabilistic models and their algorithms at a massive scale.
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UNVERDICTED 2representative citing papers
An R package that supplies ready-to-use Dirichlet process objects and handles MCMC sampling so users can fit nonparametric Bayesian models for clustering and density estimation without implementing the algorithms themselves.
citing papers explorer
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Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings
Bayesian coresets applied to network intrusion data reduce sample counts while supporting accurate posterior inference in both offline and streaming regimes.
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dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models
An R package that supplies ready-to-use Dirichlet process objects and handles MCMC sampling so users can fit nonparametric Bayesian models for clustering and density estimation without implementing the algorithms themselves.