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A general-purpose probabilistic programming system
An open-source stack for generative modeling and probabilistic inference. Gen’s inference library gives users building blocks for writing efficient probabilistic inference algorithms that are tailored to their models, while automating the tricky math and the low-level implementation details. Gen helps users write hybrid algorithms that combine neural networks, variational inference, sequential Monte Carlo samplers, and Markov chain Monte Carlo. Gen features an easy-to-use modeling language...
...Reworking Mocha with new technologies requires some non-trivial efforts, and new exciting solutions already exist nowadays, it is a good time for the retirement of Mocha.jl. Mocha has a clean architecture with isolated components like network layers, activation functions, solvers, regularizers, initializers, etc.