Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Bayes Rules! An Introduction to Applied Bayesian Modeling
Top Free Python Books 🌠 - 100% Free or Open Source!
  • Title: Bayes Rules! An Introduction to Applied Bayesian Modeling
  • Author(s) Alicia A. Johnson, Miles Q. Ott, Mine Dogucu
  • Publisher: CRC Press; 1st edition (March 4, 2022); eBook (Online Version, 2021-12-01)
  • Paperback: 521 pages
  • eBook: HTML
  • Language: English
  • ISBN-10/ASIN: 0367255391
  • ISBN-13: 978-0367255398
  • Share This:  

Book Description

An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, this book brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience.

About the Authors
  • N/A
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • An Introduction to Bayesian Thinking (Merlise Clyde, et al.)

    It provides an introduction to Bayesian Inference in decision making without requiring calculus. It may be used on its own as an open-access introduction to Bayesian inference using R Programming for anyone interested in learning about Bayesian statistics.

  • O'Reilly® Think Bayes: Bayesian Statistics in Python

    If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

  • Bayesian Reasoning and Machine Learning (David Barber)

    This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You'll develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises are provided.

  • Bayesian Methods for Statistical Analysis (Borek Puza)

    Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. It contains many exercises, all with worked solutions, including complete computer code.

  • Bayesian Data Analysis (Andrew Gelman, et al.)

    This classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. It takes an applied approach to analysis using up-to-date Bayesian methods.

  • Bayesian Methods for Hackers: Probabilistic Programming

    This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation - no advanced mathematics required.

  • Bayesian Networks and BayesiaLab (Stefan Conrady, et al.)

    This practical introduction is geared towards scientists who wish to employ Bayesian Networks for applied research using the BayesiaLab software platform. It can serve as a self-study guide for learners and as a reference manual for advanced practitioners.

  • Introduction to Modern Statistics (Mine Çetinkaya-Rundel, et al.)

    This book puts a heavy emphasis on exploratory data analysis and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches.

  • Foundations in Statistical Reasoning (Pete Kaslik)

    This book is designed for students taking an introductory statistics class. The emphasis throughout the entire book is on how to make decisions with only partial evidence. It focuses on the thought process.

  • Theory of Statistics (James E. Gentle)

    This book is directed toward students for whom mathematical statistics is or will become an important part of their lives. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics.

Book Categories
:
Other Categories
Resources and Links