Markov Random Field: Exploring the Power of Markov Random Fields in Computer Vision
About this ebook
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.
