Categorical Stochastic Processes and Likelihood
@article{Shiebler2020CategoricalSP, title={Categorical Stochastic Processes and Likelihood}, author={Dan Shiebler}, journal={ArXiv}, year={2020}, volume={abs/2005.04735} }
We take a category-theoretic perspective on the relationship between probabilistic modeling and gradient based optimization. We define two extensions of function composition to stochastic process subordination: one based on a co-Kleisli category and one based on the parameterization of a category with a Lawvere theory. We show how these extensions relate to the category of Markov kernels Stoch through a pushforward procedure.We extend stochastic processes to parametric statistical models and…
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