PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems. Check out the PyMC overview, or one of ...
Some understanding of Bayesian modeling and inference is also needed, such as the concepts of prior, likelihood, posterior, the bayes's law, and Monte Carlo sampling. Some experience with Python would also be very beneficial for readers to get started on this journey of Bayesian modeling. The ...
Welcome to the online versionBayesian Modeling and Computation in Python. If you’d like a physical copy it can purchasedfrom the publisher hereor on Amazon. This site contains an online version of the book and all the code used to produce the book. This includes the visible code, and all...
The choice to develop PyMC as a python module, rather than a standalone application, allowed the use MCMC methods in a larger modeling framework. By 2005, PyMC was reliable enough for version 1.0 to be released to the public. A small group of regular users, most associated with the ...
If you have encountered Media Mix Modeling (MMM) problems in Marketing before, you might know that these involve a whole set of channel-specific effects (delays, saturation and long-term effects) that are each modeled through at least one different parameter. You might also be aware that this...
A Unified Statistical Methodology for Modeling Fatigue Damage (Springer, Netherlands, 2009). MATH Google Scholar Castillo, E. & Fernández-Canteli, A. A compatible regression Weibull model for the description of the three-dimensional fatigue σM-N-R field as a basis for cumulative damage ...
and hypothesis testing ‒ Get started with simple examples, using coins, M\\\&Ms, Dungeons \\\& Dragons dice, paintball, and hockey ‒ Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.Down...
The objective of this course is to introduce PyMC3 for Bayesian Modeling and Inference, The attendees will start off by learning the the basics of PyMC3 and learn how to perform scalable inference for a variety of problems. This will be the final course in a specialization of three courses ...
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning Practical Bayesian Optimization of Machine Learning Algorithms Automated Machine Learning Hyperparameter Tuning in Python ...
Exoplanet: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series. Bambi: BAyesian Model-Building Interface (BAMBI) in Python. pymc3_models: Custom PyMC3 models built on top of the scikit-learn API. ...