Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Probabilistic Programming And Bayesian Methods For Hackers | 25,566 | 16 days ago | 199 | mit | Jupyter Notebook | |||||
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;) | ||||||||||
Edward | 4,503 | 39 | 2 | 4 years ago | 28 | January 22, 2018 | 218 | other | Jupyter Notebook | |
A probabilistic programming language in TensorFlow. Deep generative models, variational inference. | ||||||||||
Probability | 3,914 | 126 | 213 | 2 days ago | 43 | June 07, 2022 | 633 | apache-2.0 | Jupyter Notebook | |
Probabilistic reasoning and statistical analysis in TensorFlow | ||||||||||
Uncertainty Baselines | 1,230 | 7 days ago | 6 | July 27, 2020 | 117 | apache-2.0 | Python | |||
High-quality implementations of standard and SOTA methods on a variety of tasks. | ||||||||||
Edward2 | 633 | 1 | a month ago | 2 | May 06, 2021 | 74 | apache-2.0 | Jupyter Notebook | ||
A simple probabilistic programming language. | ||||||||||
Probflow | 108 | 2 years ago | 12 | December 28, 2020 | 16 | mit | Python | |||
A Python package for building Bayesian models with TensorFlow or PyTorch | ||||||||||
Toolbox | 104 | 2 years ago | 46 | apache-2.0 | Java | |||||
A Java Toolbox for Scalable Probabilistic Machine Learning | ||||||||||
Birch | 95 | 2 months ago | 3 | apache-2.0 | C++ | |||||
A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods. | ||||||||||
Bayesian Cognitive Modeling In Pymc3 | 90 | 6 years ago | Jupyter Notebook | |||||||
PyMC3 codes of Lee and Wagenmakers' Bayesian Cognitive Modeling - A Pratical Course | ||||||||||
19 Questions | 15 | 10 months ago | 1 | mit | PHP | |||||
A machine learning / bayesian inference engine assigning attributes to objects |
The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion.
After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap.
If Bayesian inference is the destination, then mathematical analysis is a particular path towards it. On the other hand, computing power is cheap enough that we can afford to take an alternate route via probabilistic programming. The latter path is much more useful, as it denies the necessity of mathematical intervention at each step, that is, we remove often-intractable mathematical analysis as a prerequisite to Bayesian inference. Simply put, this latter computational path proceeds via small intermediate jumps from beginning to end, where as the first path proceeds by enormous leaps, often landing far away from our target. Furthermore, without a strong mathematical background, the analysis required by the first path cannot even take place.
Bayesian Methods for Hackers is designed as an introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.
The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanations in the PyMC universe. The official documentation assumes prior knowledge of Bayesian inference and probabilistic programming. We hope this book encourages users at every level to look at PyMC. Secondly, with recent core developments and popularity of the scientific stack in Python, PyMC is likely to become a core component soon enough.
PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy, SciPy and Matplotlib.
Bayesian Methods for Hackers is now available as a printed book! You can pick up a copy on Amazon. What are the differences between the online version and the printed version?
See the project homepage here for examples, too.
The below chapters are rendered via the nbviewer at nbviewer.jupyter.org/, and is read-only and rendered in real-time. Interactive notebooks + examples can be downloaded by cloning!
Prologue: Why we do it.
Chapter 1: Introduction to Bayesian Methods Introduction to the philosophy and practice of Bayesian methods and answering the question, "What is probabilistic programming?" Examples include:
Chapter 2: A little more on PyMC We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include:
Chapter 3: Opening the Black Box of MCMC We discuss how MCMC operates and diagnostic tools. Examples include:
Chapter 4: The Greatest Theorem Never Told We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers. Examples include:
Chapter 5: Would you rather lose an arm or a leg? The introduction of loss functions and their (awesome) use in Bayesian methods. Examples include:
Chapter 6: Getting our prior-ities straight Probably the most important chapter. We draw on expert opinions to answer questions. Examples include:
We explore useful tips to be objective in analysis as well as common pitfalls of priors.
Prologue: Why we do it.
Chapter 1: Introduction to Bayesian Methods Introduction to the philosophy and practice of Bayesian methods and answering the question, "What is probabilistic programming?" Examples include:
Chapter 2: A little more on PyMC We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include:
Chapter 3: Opening the Black Box of MCMC We discuss how MCMC operates and diagnostic tools. Examples include:
Chapter 4: The Greatest Theorem Never Told We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers. Examples include:
Chapter 5: Would you rather lose an arm or a leg? The introduction of loss functions and their (awesome) use in Bayesian methods. Examples include:
Chapter 6: Getting our prior-ities straight Probably the most important chapter. We draw on expert opinions to answer questions. Examples include:
We explore useful tips to be objective in analysis as well as common pitfalls of priors.
More questions about PyMC? Please post your modeling, convergence, or any other PyMC question on cross-validated, the statistics stack-exchange.
The book can be read in three different ways, starting from most recommended to least recommended:
The most recommended option is to clone the repository to download the .ipynb files to your local machine. If you have Jupyter installed, you can view the chapters in your browser plus edit and run the code provided (and try some practice questions). This is the preferred option to read this book, though it comes with some dependencies.
(your-virtualenv) ~/path/to/the/book/Chapter1_Introduction $ jupyter notebook
The second, preferred, option is to use the nbviewer.jupyter.org site, which display Jupyter notebooks in the browser (example). The contents are updated synchronously as commits are made to the book. You can use the Contents section above to link to the chapters.
PDFs are the least-preferred method to read the book, as PDFs are static and non-interactive. If PDFs are desired, they can be created dynamically using the nbconvert utility.
If you would like to run the Jupyter notebooks locally, (option 1. above), you'll need to install the following:
Jupyter is a requirement to view the ipynb files. It can be downloaded here
Necessary packages are PyMC, NumPy, SciPy and Matplotlib.
New to Python or Jupyter, and help with the namespaces? Check out this answer.
In the styles/ directory are a number of files that are customized for the notebook. These are not only designed for the book, but they offer many improvements over the default settings of matplotlib and the Jupyter notebook. The in notebook style has not been finalized yet.
This book has an unusual development design. The content is open-sourced, meaning anyone can be an author. Authors submit content or revisions using the GitHub interface.
these are satirical, but real
"No, but it looks good" - John D. Cook
"I ... read this book ... I like it!" - Andrew Gelman
"This book is a godsend, and a direct refutation to that 'hmph! you don't know maths, piss off!' school of thought... The publishing model is so unusual. Not only is it open source but it relies on pull requests from anyone in order to progress the book. This is ingenious and heartening" - excited Reddit user
Thanks to all our contributing authors, including (in chronological order):
We would like to thank the Python community for building an amazing architecture. We would like to thank the statistics community for building an amazing architecture.
Similarly, the book is only possible because of the PyMC library. A big thanks to the core devs of PyMC: Chris Fonnesbeck, Anand Patil, David Huard and John Salvatier.
One final thanks. This book was generated by Jupyter Notebook, a wonderful tool for developing in Python. We thank the IPython/Jupyter community for developing the Notebook interface. All Jupyter notebook files are available for download on the GitHub repository.
Contact the main author, Cam Davidson-Pilon at [email protected] or @cmrndp