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Bayesian Thinking (Bayesian Method, Bayesian Inference, etc.)
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  • An Introduction to Bayesian Thinking (Merlise Clyde, et al.)

    This book 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 for anyone interested in learning about Bayesian statistics.

  • 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 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 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 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 Methods in the Search for MH370 (Samuel Davey, et al.)

    This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths, which was used to define the search zone in the southern Indian Ocean.

  • 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 Computational Methods (Christian P. Robert)

    This monograpg presents the most standard computational challenges met in Bayesian Statistics, focussing primarily on mixture estimation and on model choice issues, and then relate these problems with computational solutions.

  • Kalman and Bayesian Filters in Python (Roger R Labbe Jr.)

    This book is an introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn?

  • Pattern Recognition and Machine Learning: Bayesian Viewpoint

    This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

  • Nonparametric Bayesian Learning for Collaborative Robot

    This book introduces a fast, accurate, robot anomaly monitoring, diagnosis and recovery scheme for endowing robots with longer-term autonomy and a safer collaborative environment, emonstrates two robots that perform three manipulation tasks.

  • Bayesian Field Theory (Jorg C. Lemm)

    Long the province of mathematicians and statisticians, Bayesian methods are applied in this ground-breaking book to problems in cutting-edge physics, with practical examples of Bayesian analysis for the physicist working in such areas as neural networks, artificial intelligence, and inverse problems in quantum theory.

  • Bayesian Spectrum Analysis and Parameter Estimation

    This work is primarily on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis; consequently.

  • Dynamic Programming and Bayesian Inference, Concepts/Apps

    This is a book on the far-ranging algorithmic methododogy of Dynamic Programming. It presents a comprehensive and rigorous treatment of dynamic programming, and provides some applications of Bayesian optimization and dynamic programming.

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