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Bayesian inference is a type of statistical inference that updates the probability of a hypothesis based on new data or information using Bayes' theorem. This way of statistical inference is known as the Bayesian method. In the field of statistics, and particularly in the field of mathematical statistics, the Bayesian inference method is an essential tool. When conducting a dynamic analysis of a data sequence, bayesian updating is an especially useful technique to utilize. Inference based on Bayes' theorem has been successfully implemented in a diverse range of fields, including those of science, engineering, philosophy, medicine, athletics, and the legal system. Bayesian inference is strongly related to subjective probability, which is why it is frequently referred to as "Bayesian probability" in the field of decision theory philosophy.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Bayesian Inference
Chapter 2: Likelihood Function
Chapter 3: Conjugate Prior
Chapter 4: Posterior Probability
Chapter 5: Maximum a Posteriori Estimation
Chapter 6: Bayes Estimator
Chapter 7: Bayesian Linear Regression
Chapter 8: Dirichlet Distribution
Chapter 9: Variational Bayesian Methods
Chapter 10: Bayesian Hierarchical Modeling
(II) Answering the public top questions about bayesian inference.
(III) Real world examples for the usage of bayesian inference in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of bayesian inference' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of bayesian inference.
