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update api docs for distributions
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‎.gitignore

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# Sphinx
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_build
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docs/jupyter_execute
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docs/**/generated/*
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# Merge tool
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*.orig

‎docs/source/api/distributions/continuous.rst

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Continuous
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**********
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.. currentmodule:: pymc.distributions.continuous
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated/
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Uniform
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Flat
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Moyal
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AsymmetricLaplace
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PolyaGamma
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.. automodule:: pymc.distributions.continuous
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:members:

‎docs/source/api/distributions/discrete.rst

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Discrete
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********
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.. currentmodule:: pymc.distributions.discrete
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated
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Binomial
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BetaBinomial
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Categorical
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OrderedLogistic
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OrderedProbit
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.. automodule:: pymc.distributions.discrete
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:members:

‎docs/source/api/distributions/mixture.rst

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Mixture
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*******
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.. currentmodule:: pymc.distributions.mixture
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated
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Mixture
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NormalMixture
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MixtureSameFamily
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.. automodule:: pymc.distributions.mixture
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:members:

‎docs/source/api/distributions/multivariate.rst

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Multivariate
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************
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.. currentmodule:: pymc.distributions.multivariate
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated
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MvNormal
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MvStudentT
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MatrixNormal
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KroneckerNormal
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CAR
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.. automodule:: pymc.distributions.multivariate
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:members:

‎docs/source/api/distributions/simulator.rst

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.. currentmodule:: pymc.distributions.simulator
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.. autosummary::
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:toctree: generated
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SimulatorRV
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Simulator
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KullbackLiebler
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.. automodule:: pymc.distributions.simulator
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:members:

‎docs/source/api/distributions/timeseries.rst

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.. currentmodule:: pymc.distributions.timeseries
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.. autosummary::
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:toctree: generated
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AR1
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AR
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GaussianRandomWalk
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GARCH11
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EulerMaruyama
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MvGaussianRandomWalk
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MvStudentTRandomWalk
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.. automodule:: pymc.distributions.timeseries
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:members:

‎docs/source/api/distributions/transforms.rst

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****************************************************************
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Transformations of a random variable from one space to another
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****************************************************************
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Note that for convenience these entities can be addressed as
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``pm.transforms.``\ *X* for any name *X*, although they are actually
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implemented as ``pm.distributions.transforms.``\ *X*.
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.. currentmodule:: pymc.distributions.transforms
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.. contents ::
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..
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.. autosummary::
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Transform
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stick_breaking
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logodds
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interval
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log_exp_m1
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ordered
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log
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sum_to_1
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circular
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CholeskyCovPacked
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Chain
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***************
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Transformations
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***************
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.. currentmodule:: pymc.transforms
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Transform Instances
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~~~~~~~~~~~~~~~~~~~
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Transform instances are the entities that should be used in the
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``transform`` parameter to a random variable constructor. These are
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initialized instances of the Transform Classes, which are described
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below.
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.. glossary::
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``stick_breaking``
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Instantiation of :class:`~pymc.distributions.transforms.StickBreaking`
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:class:`~pymc.distributions.transforms.Transform` class for use in the ``transform``
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argument of a random variable.
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``logodds``
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Instantiation of
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:class:`~pymc.distributions.transforms.LogOdds` :class:`~pymc.distributions.transforms.Transform` class
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for use in the ``transform`` argument of a random variable.
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``interval``
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Alias of
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:class:`~pymc.distributions.transforms.Interval` :class:`~pymc.distributions.transforms.Transform` class
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for use in the ``transform`` argument of a random variable.
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``log_exp_m1``
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Instantiation of
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:class:`~pymc.distributions.transforms.LogExpM1` :class:`~pymc.distributions.transforms.Transform` class
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for use in the ``transform`` argument of a random variable.
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``transform`` parameter to a random variable constructor.
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``lowerbound``
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Alias of
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:class:`~pymc.distributions.transforms.LowerBound` :class:`~pymc.distributions.transforms.Transform` class
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for use in the ``transform`` argument of a random variable.
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``upperbound``
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Alias of
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:class:`~pymc.distributions.transforms.UpperBound` :class:`~pymc.distributions.transforms.Transform` class
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for use in the ``transform`` argument of a random variable.
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``ordered``
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Instantiation of
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:class:`~pymc.distributions.transforms.Ordered` :class:`~pymc.distributions.transforms.Transform` class
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for use in the ``transform`` argument of a random variable.
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``log``
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Instantiation of
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:class:`~pymc.distributions.transforms.Log` :class:`~pymc.distributions.transforms.Transform` class
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for use in the ``transform`` argument of a random variable.
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``sum_to_1``
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Instantiation of
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:class:`~pymc.distributions.transforms.SumTo1` :class:`~pymc.distributions.transforms.Transform` class
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for use in the ``transform`` argument of a random variable.
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``circular``
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Instantiation of
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:class:`~pymc.distributions.transforms.Circular` :class:`~pymc.distributions.transforms.Transform` class
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for use in the ``transform`` argument of a random variable.
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Transform Base Classes
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~~~~~~~~~~~~~~~~~~~~~~
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Typically the programmer will not use these directly.
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.. autoclass:: Transform
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:members:
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.. autosummary::
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:toctree: generated
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simplex
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logodds
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interval
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log_exp_m1
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ordered
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log
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sum_to_1
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circular
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Transform Composition Classes
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: Chain
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:members:
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.. autoclass:: CholeskyCovPacked
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:members:
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.. autosummary::
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:toctree: generated
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Chain
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CholeskyCovPacked
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Specific Transform Classes
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: Log
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:members:
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.. autoclass:: LogExpM1
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:members:
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.. autoclass:: LogOdds
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:members:
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.. autoclass:: Interval
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:members:
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.. autoclass:: Ordered
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:members:
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.. autoclass:: SumTo1
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:members:
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.. autoclass:: StickBreaking
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:members:
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.. autoclass:: Circular
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:members:
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.. autosummary::
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:toctree: generated
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LogExpM1
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Ordered
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SumTo1

‎docs/source/api/distributions/utilities.rst

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*******************************************
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Distribution utility classes and functions
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*******************************************
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**********************
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Distribution utilities
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**********************
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.. currentmodule:: pymc.distributions
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.. currentmodule:: pymc
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.. autosummary::
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:toctree: generated
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Distribution
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Discrete
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Continuous
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NoDistribution
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DensityDist
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.. autoclass:: Distribution
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.. autoclass:: Discrete
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.. autoclass:: Continuous
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.. autoclass:: NoDistribution
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.. autoclass:: DensityDist
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:members:

‎docs/source/conf.py

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# myst and panels config
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jupyter_execute_notebooks = "off"
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myst_enable_extensions = ["colon_fence", "deflist", "dollarmath", "amsmath", "substitution"]
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panels_add_bootstrap_css = False
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myst_heading_anchors = 0
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# The reST default role (used for this markup: `text`) to use for all
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# documents.

‎docs/source/index.md

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Salvatier J., Wiecki T.V., Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 [DOI: 10.7717/peerj-cs.55.](https://doi.org/10.7717/peerj-cs.55)
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For detailed advise on citing PyMC go to {ref
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For detailed advise on citing PyMC go to {ref}`citing_pymc`.
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See [Google Scholar](https://scholar.google.de/scholar?oi=bibs&hl=en&authuser=1&cites=6936955228135731011) for a continuously updated list of papers citing PyMC3.
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‎pymc/distributions/transforms.py

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simplex = Simplex()
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logodds = LogOddsTransform()
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logodds.__doc__ = """
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Instantiation of :class:`aeppl.transforms.LogOddsTransform`
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for use in the ``transform`` argument of a random variable."""
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interval = IntervalTransform
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interval.__doc__ = """
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Instantiation of :class:`aeppl.transforms.IntervalTransform`
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for use in the ``transform`` argument of a random variable."""
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log_exp_m1 = LogExpM1()
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log_exp_m1.__doc__ = """
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Instantiation of :class:`pymc.transforms.LogExpM1`
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for use in the ``transform`` argument of a random variable."""
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ordered = Ordered()
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ordered.__doc__ = """
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Instantiation of :class:`pymc.transforms.Ordered`
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for use in the ``transform`` argument of a random variable."""
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log = LogTransform()
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log.__doc__ = """
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Instantiation of :class:`aeppl.transforms.LogTransform`
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for use in the ``transform`` argument of a random variable."""
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sum_to_1 = SumTo1()
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sum_to_1.__doc__ = """
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Instantiation of :class:`pymc.transforms.SumTo1`
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for use in the ``transform`` argument of a random variable."""
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circular = CircularTransform()
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circular.__doc__ = """
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Instantiation of :class:`aeppl.transforms.CircularTransform`
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for use in the ``transform`` argument of a random variable."""

‎pymc/model.py

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"""Encapsulates the variables and likelihood factors of a model.
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Model class can be used for creating class based models. To create
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a class based model you should inherit from :class:`~pymc.Model` and
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override the `__init__` method with arbitrary definitions (do not
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CustomModel(mean=1, name='first')
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CustomModel(mean=2, name='second')
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"""
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if TYPE_CHECKING:

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