• Corpus ID: 220363617

The FMRIB Variational Bayesian Inference Tutorial II: Stochastic Variational Bayes

@article{Chappell2020TheFV,
  title={The FMRIB Variational Bayesian Inference Tutorial II: Stochastic Variational Bayes},
  author={Michael A. Chappell and Mark W. Woolrich},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.02725}
}
Bayesian methods have proved powerful in many applications for the inference of model parameters from data. These methods are based on Bayes' theorem, which itself is deceptively simple. However, in practice the computations required are intractable even for simple cases. Hence methods for Bayesian inference have historically either been significantly approximate, e.g., the Laplace approximation, or achieve samples from the exact solution at significant computational expense, e.g., Markov Chain… 

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References

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