`_, it enables you to implement anything you could have built in the base language.
-- *Performance*. The primary inference algorithm is gradient-based automatic differentiation variational inference (ADVI) (Kucukelbir et al., 2017), which estimates a divergence measure between approximate and true posterior distributions. Pymc-learn scales to complex, high-dimensional models thanks to GPU-accelerated tensor math and reverse-mode automatic differentiation via Theano (Theano Development Team, 2016), and it scales to large datasets thanks to estimates computed over mini-batches of data in ADVI.
+- *Performance*. The primary inference algorithm is gradient-based automatic differentiation variational inference (ADVI) (Kucukelbir et al., 2017), which estimates a divergence measure between approximate and true posterior distributions. Pymc-learn scales to complex, high-dimensional models thanks to GPU-accelerated tensor math and reverse-mode automatic differentiation via TensorFlow (TensorFlow Development Team, 2016), and it scales to large datasets thanks to estimates computed over mini-batches of data in ADVI.
----
@@ -75,18 +75,9 @@ on:
(6) an API consistent with scikit-learn.
-The underlying probabilistic models are built using pymc3 (Salvatier et al., 2016).
+The underlying probabilistic models are built using pymc4 (Salvatier et al., 2019).
-Transitioning from PyMC3 to PyMC4
-..................................
-
-.. raw:: html
-
-
-
Python is the lingua franca of Data Science
--------------------------------------------
@@ -112,27 +103,17 @@ notebooks, collaboration, and so forth.
----
-Why scikit-learn and PyMC3
+Why scikit-learn and PyMC4
---------------------------
-PyMC3 is a Python package for probabilistic machine learning that enables users
+PyMC4 is a Python package for probabilistic machine learning that enables users
to build bespoke models for their specific problems using a probabilistic
-modeling framework. However, PyMC3 lacks the steps between creating a model and
-reusing it with new data in production. The missing steps include: scoring a
-model, saving a model for later use, and loading the model in production
-systems.
+modeling framework.
-In contrast, *scikit-learn* which has become the standard
+*scikit-learn* which has become the standard
library for machine learning provides a simple API that makes it very easy for
-users to train, score, save and load models in production. However,
-*scikit-learn* may not have the model for a user's specific problem.
-These limitations have led to the development of the open
-source *pymc3-models* library which provides a template to build bespoke
-PyMC3 models on top of the *scikit-learn* API and reuse them in
-production. This enables users to easily and quickly train, score, save and
-load their bespoke models just like in *scikit-learn*.
-
-The ``pymc-learn`` project adopted and extended the template in *pymc3-models*
-to develop probabilistic versions of the estimators in *scikit-learn*.
+users to train, score, save and load models in production.
+
+The ``pymc-learn`` project developed probabilistic versions of the estimators in *scikit-learn*.
This provides users with probabilistic models in a simple workflow that mimics
the scikit-learn API.
@@ -190,10 +171,10 @@ References
4. Barber, D. (2012). Bayesian reasoning and machine learning. Cambridge University Press.
-5. Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. (2016). Probabilistic programming in Python using PyMC3. PeerJ Computer Science, 2, e55.
+5. PyMC4.
6. Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M Blei. Automatic differentiation variational inference. The Journal of Machine Learning Research, 18(1):430{474, 2017.
7. Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. Journal of machine learning research, 12(Oct): 2825-2830, 2011.
-8. Theano Development Team. Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints, abs/1605.02688, May 2016. URL http://arxiv.org/abs/1605.02688.
\ No newline at end of file
+8. TensorFlow
\ No newline at end of file
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