Bayesian Learning

In the field of statistics, an expectation-maximization (EM) algorithm is an iterative approach to discover (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM algorithms are also known as maximum likelihood or maximum a posteriori (MAP) estimations. The expectation (E) step of the EM iteration creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and the maximization (M) step of the EM iteration computes parameters with the goal of maximizing the expected log-likelihood found on the expectation step. These two steps are performed in alternating fashion throughout the iteration. These parameter-estimates are then utilized in the subsequent E phase, which serves the purpose of determining the distribution of the latent variables.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Expectation-maximization algorithm

Chapter 2: Likelihood function

Chapter 3: Maximum likelihood estimation

Chapter 4: Logistic regression

Chapter 5: Exponential family

Chapter 6: Fisher information

Chapter 7: Generalized linear model

Chapter 8: Mixture model

Chapter 9: Variational Bayesian methods

Chapter 10: EM algorithm and GMM model

(II) Answering the public top questions about bayesian learning.

(III) Real world examples for the usage of bayesian learning in many fields.

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 learning.

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