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stan
Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
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InfluxDB
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This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing
This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing
This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing
This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing
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