Skip to content

Implement the stochastic simulation algorithm for obtaining the most probable configuration of a 1st order regular MRF considering the at least two variants ICM and Metropolis, using MAP

Notifications You must be signed in to change notification settings

MarSH-Up/MRFs_Stochastic-Simulation

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

19 Commits
 
 
 
 
 
 
 
 

Repository files navigation

⛰ Markov Random Field Optimizacion using Stochactic Seach

The content of this repository served as an assignment project requested for the course Probabilistic Graphical Models at the INAOE as a student of the Master in Science in Computer Science. All the resources presented in the versions of this code were obtained from the class book that you can find in the references part.

This application of the algorithm and information was for an only educational purpose

Description:

Implement the stochastic simulation algorithm for obtaining the most probable configuration of a 1st order regular MRF considering the at least two variantes ICM and Metropolis, using MAP.

Professor:

Student Involved:

Instructions

  1. Download the repository's file
  2. Verify that the C++ version is at least C++ 14
  3. Call the functions marked in the documentation

Example We run some examples, you can find the document here, the idea is give you a more detail approach to the replication, also, in the images folder you can find the exercise used. The following exercise if also in the documentation folder. example The next figures, shows the implementation using the two methods in the class, MAP and Metroplis.

#References

  • Sucar, L. E. (2020). Probabilistic graphical models. Advances in Computer Vision and Pattern Recognition.London: Springer London. doi, 10(978), 2

#Licence: GNU GENERAL PUBLIC LICENSE Version 3

About

Implement the stochastic simulation algorithm for obtaining the most probable configuration of a 1st order regular MRF considering the at least two variants ICM and Metropolis, using MAP

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages