STA-663-2017 1.0 documentation » Using PyMC3 ¶ PyMC3 is a Python package for doing MCMC using a variety of samplers, including Metropolis, Slice and Hamiltonian Monte Carlo.
Check out the PyMC3 documentation for permissible values for the inference_type you are using. Building your own models ¶ Lastly, if you want to build your own models, you can build them on top of...

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Sometimes an unknown parameter or variable in a model is not a scalar value or a fixed-length vector, but a function. A Gaussian process (GP) can be used as a prior probability distribution whose support is over the space of continuous functions.

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Transitioning from PyMC3 to PyMC4¶ . @pymc_learn has been following closely the development of #PyMC4 with the aim of switching its backend from #PyMC3 to PyMC4 as the latter grows to maturity. Core devs are invited. PyMC3 Documentation — PyMC3 3.9.3 documentation. is tracked by us since September, 2017. Over the time it has been ranked as high as 252 299 in the world...

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One easy way of developing on PyMC4 is to take advantage of the development containers! Using pre-built development environments allows you to develop on PyMC4 without needing to set up locally. To use the dev containers, you will need to have Docker and VSCode running locally on your machine, and will need the VSCode Remote extension ( ms ... Transitioning from PyMC3 to PyMC4¶ . @pymc_learn has been following closely the development of #PyMC4 with the aim of switching its backend from #PyMC3 to PyMC4 as the latter grows to maturity. Core devs are invited.

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Experimental PyMC interface for TensorFlow Probability. Official work on this project has been discontinued. - pymc-devs/pymc4

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