• Corpus ID: 116983370

Bayesian machine learning via category theory

@article{Culbertson2013BayesianML,
  title={Bayesian machine learning via category theory},
  author={Jared Culbertson and Kirk Sturtz},
  journal={arXiv: Category Theory},
  year={2013}
}
From the Bayesian perspective, the category of conditional probabilities (a variant of the Kleisli category of the Giry monad, whose objects are measurable spaces and arrows are Markov kernels) gives a nice framework for conceptualization and analysis of many aspects of machine learning. Using categorical methods, we construct models for parametric and nonparametric Bayesian reasoning on function spaces, thus providing a basis for the supervised learning problem. In particular, stochastic… 
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