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Bump for 4.0.0b2 (#5348)
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README.rst

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@@ -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.
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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
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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

RELEASE-NOTES.md

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⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠⚠
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-->
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## PyMC vNext (4.0.0b1 → 4.0.0b2 → 4.0.0)
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## PyMC vNext (4.0.0b1 → 4.0.0b2 → 4.0.0b3 → 4.0.0)
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⚠ The changes below are the delta between the upcoming releases `v3.11.5` →...→ `v4.0.0`.
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### No-yet working features

pymc/__init__.py

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# limitations under the License.
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# pylint: disable=wildcard-import
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__version__ = "4.0.0b1"
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__version__ = "4.0.0b2"
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import logging
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import multiprocessing as mp

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