What is Markov Random Field

In the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. In other words, a random field is said to be a Markov random field if it satisfies Markov properties. The concept originates from the Sherrington-Kirkpatrick model.

How you will benefit

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

Chapter 1: Markov random field

Chapter 2: Multivariate random variable

Chapter 3: Hidden Markov model

Chapter 4: Bayesian network

Chapter 5: Graphical model

Chapter 6: Random field

Chapter 7: Belief propagation

Chapter 8: Factor graph

Chapter 9: Conditional random field

Chapter 10: Hammersley-Clifford theorem

(II) Answering the public top questions about markov random field.

(III) Real world examples for the usage of markov random field 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 Markov Random Field.