Section 3: Hierarchal modeling Model pooling (separate models) Partial pooling (hierarchal models) Shrinkage effect of partial pooling Section 4: Bayesian regression Bayesian fixed effects poisson regression Bayesian mixed effects poisson regression
Texts in Statistical Science(共58册), 这套丛书还有 《Bayesian Statistical Methods》《Bayesian Ideas and Data Analysis》《Sampling: Design and Analysis》《Mathematical Statistics》《Modern Data Science with R (2/e)》等。 当前版本有售· ··· 京东商城...
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 ...
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: Detecting the frequency of cheating students, while avoiding liars Calculating probabilities of the Challenger space-shuttle disaster...
This book is useful for readers who want to hone their skills in Bayesian modeling and computation. Written by experts in the area of Bayesian software and major contributors to some existing widely used Bayesian computational tools, this book covers not only basic Bayesian probabilistic inference bu...
This comprehensive guide is designed for anyone who wants to delve into the practical application of Bayesian methods for modeling sports data。 You will be exposed to a concise yet practical sequence of statistical concepts that get you on the path to sports modeling in R as quickly as possible...
Bayesian linear regression solves the problem of overfitting in maximum likelihood estimation. Moreover, it makes full use of data samples and is suitable for modeling complex data [18,19]. In addition to regression, Bayesian reasoning can also be applied in other fields. Some researchers have ...
This comprehensive guide is designed for anyone who wants to delve into the practical application of Bayesian methods for modeling sports data。 You will be exposed to a concise yet practical sequence of statistical concepts that get you on the path to sports modeling in R as quickly as ...
(particularly ecologists). 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...