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Probabilistic programming in Python using PyMC3

John Salvatier, Thomas V. Wiecki, Christopher Fonnesbeck
2016 PeerJ Computer Science  
PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly  ...  Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code.  ...  Probabilistic programming in Python (Python Software Foundation, 2010) confers a number of advantages including multi-platform compatibility, an expressive yet clean and readable syntax, easy integration  ... 
doi:10.7717/peerj-cs.55 fatcat:5dnl6podevb4vgaqfmywwlbcdi

Pymc-learn: Practical Probabilistic Machine Learning in Python [article]

Daniel Emaasit
2018 arXiv   pre-print
It depends on scikit-learn and pymc3 and is distributed under the new BSD-3 license, encouraging its use in both academia and industry.  ...  Pymc-learn is a Python package providing a variety of state-of-the-art probabilistic models for supervised and unsupervised machine learning.  ...  Acknowledgments We would like to acknowledge the scikit-learn, pymc3 and pymc3-models communities for open-sourcing their respective Python packages.  ... 
arXiv:1811.00542v1 fatcat:liryu44hcbhsfooxpopsifsmgi

Comparison of software packages for performing Bayesian inference

Marko Koprivica
2020 Neural Network World  
In this paper, we compare three state-of-the-art Python packages for Bayesian inference: JAGS [14] , Stan [5], and PyMC3 [18].  ...  These packages are in focus because they are the most mature, and Python is among the most utilized programming languages for teaching mathematics and statistics in colleges [13] .  ...  Program availability The source code of the program written in Python for this experiment and data results is available from https://github.com/koprivica/MCMC-modules-comparison under the MIT license.  ... 
doi:10.14311/nnw.2020.30.019 fatcat:rdd4qhw3oreoripe5hwitzgphq

PyAutoFit: A Classy Probabilistic Programming Language for Model Composition and Fitting

James. Nightingale, Richard Hayes, Matthew Griffiths
2021 Journal of Open Source Software  
A major trend in academia and data science is the rapid adoption of Bayesian statistics for data analysis and modeling, leading to the development of probabilistic programming languages (PPL).  ...  A PPL provides a framework that allows users to easily specify a probabilistic model and perform inference automatically.  ...  Background of Probabilistic Programming Probabilistic programming languages (PPLs) have enabled contemporary statistical inference techniques to be applied to a diverse range of problems across academia  ... 
doi:10.21105/joss.02550 fatcat:2mp2gdqgazckxbfzssxoktt7d4

Probabilistic Programming and PyMC3 [article]

Peadar Coyle
2016 arXiv   pre-print
This is intended to be a brief introduction to Probabilistic Programming in Python and in particular the powerful library called PyMC3.  ...  In recent years sports analytics has gotten more and more popular. We propose a model for Rugby data - in particular to model the 2014 Six Nations tournament.  ...  PyMC3 despite being at the time of writing in beta is a useful framework for building Probabilistic Programming models.  ... 
arXiv:1607.00379v1 fatcat:smset52lpvdqbfq475mbapprwu

Probabilistic Programming in Python using PyMC [article]

John Salvatier, Thomas Wiecki, Christopher Fonnesbeck
2015 arXiv   pre-print
Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code.  ...  Probabilistic programming in Python confers a number of advantages including multi-platform compatibility, an expressive yet clean and readable syntax, easy integration with other scientific libraries,  ...  However, the library of functions in Theano is not exhaustive, therefore Theano and PyMC3 provide functionality for creating arbitrary Theano functions in pure Python, and including these functions in  ... 
arXiv:1507.08050v1 fatcat:vhmo7k5qdjh6zh3hx7ggd3di2y

Evaluating probabilistic programming languages for simulating quantum correlations [article]

Abdul Obeid, Peter D. Bruza, Peter Wittek
2018 PLoS ONE   pre-print
This article explores how probabilistic programming can be used to simulate quantum correlations in an EPR experimental setting.  ...  Four contemporary open source probabilistic programming frameworks were used to simulate an EPR experiment in order to shed light on their relative effectiveness from both qualitative and quantitative  ...  The question arises as to how to simulate such correlations using probabilistic programming.  ... 
doi:10.1371/journal.pone.0208555 pmid:30608937 pmcid:PMC6319741 arXiv:1811.04424v1 fatcat:ldhgxed2ovfnhf3hnjd72fcvem

BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data [article]

Karine Tung, Steven De La Torre, Mohamed El Mistiri, Rebecca Braga De Braganca, Eric Hekler, Misha Pavel, Daniel Rivera, Pedja Klasnja, Donna Spruijt-Metz, Benjamin M. Marlin
2022 arXiv   pre-print
coupled with a compiler that can produce optimized probabilistic program code for performing inference in the specified model.  ...  computationally efficient probabilistic inference code.  ...  BayesLDM currently supports compiling BayesLDM models into Python programs using NumPyro as the probabilistic inference library [19] .  ... 
arXiv:2209.05581v1 fatcat:xvgmlcebcvc6riyansusabloha

Increasing Interpretability of Bayesian Probabilistic Programming Models Through Interactive Representations

Evdoxia Taka, Sebastian Stein, John H. Williamson
2020 Frontiers in Computer Science  
To support this, we see a need for visualization tools that make probabilistic programs interpretable to reveal the interdependencies in probabilistic models and their inherent uncertainty.  ...  We propose the automatic transformation of Bayesian probabilistic models, expressed in a probabilistic programming language, into an interactive graphical representation of the model's structure at varying  ...  Figure 3C presents the probabilistic graphical model of the same model using the PyMC3 Graphviz interface 13 .  ... 
doi:10.3389/fcomp.2020.567344 fatcat:aakzmrx3fncc3mbmhowcfbeemy

Simple, Distributed, and Accelerated Probabilistic Programming [article]

Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous
2018 arXiv   pre-print
In particular, we distill probabilistic programming down to a single abstraction---the random variable.  ...  We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem.  ...  Random Variables Are All You Need We outline probabilistic programs in Edward2. They require only one abstraction: a random variable.  ... 
arXiv:1811.02091v2 fatcat:gzfjqqs4ujfzllyskht4v3al64

Yaps: Python Frontend to Stan [article]

Guillaume Baudart, Martin Hirzel, Kiran Kate, Louis Mandel, Avraham Shinnar
2018 arXiv   pre-print
Unfortunately, existing embeddings of Stan in Python use multi-line strings.  ...  Stan is a popular probabilistic programming language with a self-contained syntax and semantics that is close to graphical models.  ...  Figure 2 shows the PyCmdStan version of Cells 1-4 of the Yaps example from There are various other Python-embedded probabilistic programming languages, such as PyMC3 [13] , Edward [14] , and Pyro  ... 
arXiv:1812.04125v1 fatcat:g6nrex6abjbkvclqmep37mmy6e

ArviZ a unified library for exploratory analysis of Bayesian models in Python

Ravin Kumar, Colin Carroll, Ari Hartikainen, Osvaldo Martin
2019 Journal of Open Source Software  
Probabilistic programming languages (PPLs) implement functions to easily build Bayesian models together with efficient automatic inference methods.  ...  In the words of Persi Diaconis (Diaconis, 2011) "Exploratory data analysis seeks to reveal structure, or simple descriptions in data. We look at numbers or graphs and try to find patterns.  ...  Example Plots Examples of ArviZ's plotting functionality are shown in Figure 2  ... 
doi:10.21105/joss.01143 fatcat:rolp4jvj5rg35cixwfc3wrc3pq

Borch: A Deep Universal Probabilistic Programming Language [article]

Lewis Belcher, Johan Gudmundsson, Michael Green
2022 arXiv   pre-print
We present Borch, a scalable deep universal probabilistic programming language, built on top of PyTorch.  ...  The code is available for download and use in our repository https://gitlab.com/desupervised/borch.  ...  To combat these issues practitioners usually revert to using a probabilistic programming language (PPL) which enables the expression of probabilistic programs and performing inference on those programs  ... 
arXiv:2209.06168v1 fatcat:472bjzwyordvljnllrvkxp6g7u

ZhuSuan: A Library for Bayesian Deep Learning [article]

Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou
2017 arXiv   pre-print
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.  ...  We use running examples to illustrate the probabilistic programming on ZhuSuan, including Bayesian logistic regression, variational auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural  ...  Youth Top-notch Talents Support Program, the NVIDIA NVAIL program, and Tsinghua Tiangong Institite for Intelligent Technology.  ... 
arXiv:1709.05870v1 fatcat:b2wopv5jgrcitbygcxh6pm7mxq

Probabilistic Inference on Noisy Time Series (PINTS)

Michael Clerx, Martin Robinson, Ben Lambert, Chon Lok Lei, Sanmitra Ghosh, Gary R. Mirams, David J. Gavaghan
2019 Journal of Open Research Software  
PINTS (Probabilistic Inference on Noisy Time Series -https://github.com/pints-team/pints) is an open-source (BSD 3-clause license) Python library that provides researchers with a broad suite of non-linear  ...  By making these statistical techniques available in an open and easy-to-use framework, PINTS brings the power of these modern methods to a wider scientific audience.  ...  Programming language PINTS requires Python 2.7 or higher, or Python 3.4 or higher.  ... 
doi:10.5334/jors.252 fatcat:iomwmxtbojc43am52kpielppzu
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