@@ -10,7 +10,7 @@ focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (
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algorithms. Its flexibility and extensibility make it applicable to a
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large suite of problems.
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- Check out the `PyMC overview <https://docs.pymc.io/en/stable /learn/examples/pymc_overview.html >`__, or
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+ Check out the `PyMC overview <https://docs.pymc.io/en/latest /learn/examples/pymc_overview.html >`__, or
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`interact with live examples <https://mybinder.org/v2/gh/pymc-devs/pymc/main?filepath=%2Fdocs%2Fsource%2Fnotebooks >`__
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using Binder!
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For questions on PyMC, head on over to our `PyMC Discourse <https://discourse.pymc.io/ >`__ forum.
@@ -29,7 +29,7 @@ Features
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for large data sets.
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- Relies on `Aesara <https://aesara.readthedocs.io/en/latest/ >`__ which provides:
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* Computation optimization and dynamic C or JAX compilation
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- * Numpy broadcasting and advanced indexing
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+ * NumPy broadcasting and advanced indexing
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* Linear algebra operators
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* Simple extensibility
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- Transparent support for missing value imputation
@@ -41,7 +41,7 @@ If you already know about Bayesian statistics:
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----------------------------------------------
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- `API quickstart guide <https://docs.pymc.io/en/stable/pymc-examples/examples/pymc3_howto/api_quickstart.html >`__
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- - The `PyMC tutorial <https://docs.pymc.io/en/stable /learn/examples/pymc_overview.html >`__
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+ - The `PyMC tutorial <https://docs.pymc.io/en/latest /learn/examples/pymc_overview.html >`__
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- `PyMC examples <https://docs.pymc.io/nb_examples/index.html >`__ and the `API reference <https://docs.pymc.io/en/stable/api.html >`__
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Learn Bayesian statistics with a book together with PyMC
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