.. image:: pystat.png
    :height: 20
    :alt: Statistique
    :target: http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/td_2a_notions.html#pour-un-profil-plutot-data-scientist

Modèles bayésiens
+++++++++++++++++

*(à venir)*

*Notebooks*

.. toctree::
    :maxdepth: 2

    ../notebooks/_gs2a_bayes

*Lectures*

* `A Bayesian Approximation Method for Online Ranking <http://jmlr.csail.mit.edu/papers/volume12/weng11a/weng11a.pdf>`_
* `stan case studies <http://mc-stan.org/documentation/case-studies>`_
* `Edward: A library for probabilistic modeling, inference, and criticism <https://arxiv.org/pdf/1610.09787.pdf>`_
* `Auto-Encoding Variational Bayes <https://arxiv.org/pdf/1312.6114.pdf>`_
* `Particle Gibbs with Ancestor Sampling <http://jmlr.org/papers/volume15/lindsten14a/lindsten14a.pdf>`_
* `Controlled Sequential Monte Carlo <https://arxiv.org/abs/1708.08396>`_
* `Functional probabilistic programming for scalable Bayesian modelling <https://arxiv.org/abs/1908.02062>`_
* `Meta-Learning Probabilistic Inference For Prediction <https://arxiv.org/abs/1805.09921>`_

*Vidéo*

* `Variational Inference in Python <https://www.youtube.com/watch?v=3KGZDC3-_iY>`_
* `Bayesian Network Modeling using R and Python <https://www.youtube.com/watch?v=iRvXfx9IWM0>`_

*Modules*

* `edward <http://edwardlib.org/>`_
* `elfi <https://elfi.readthedocs.io/en/latest/index.html>`_
* `PyMC3 <https://pymc-devs.github.io/pymc3/notebooks/getting_started.html>`_
* `bayespy <http://bayespy.org/en/latest/>`_
* `kabuki <https://pypi.python.org/pypi/kabuki/>`_
* `bnpy <https://github.com/bnpy/bnpy>`_
* `pyro <http://pyro.ai/>`_ : modèle bayèsiens et deep learning
* `arviz <https://arviz-devs.github.io/arviz/>`_
* `BayesianOptimization <https://github.com/fmfn/BayesianOptimization>`_,
  optimisation bayésienne en python pur

*Exemples de code*

* `Probabilistic Models <https://github.com/wiseodd/probabilistic-models>`_ :
  sont implémentés entre autres, LDA, Chinese Restaurant Process, Indian Restaurant Process,
  GMM...