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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood
X Descombes1, R D Morris, J Zerubia
1Inst. Nat. de Recherche en Inf. et Autom., Sophia Antipolis, France.
Summary
New statistical methods enable maximum likelihood estimation for Markov random fields (MRFs). An efficient algorithm is presented and demonstrated on various MRF models for image analysis and parameter estimation.
Area of Science:
- Statistics
- Computer Vision
- Image Analysis
Background:
- Markov random fields (MRFs) are widely used for image modeling.
- Parameter estimation for MRFs has been a challenging problem.
Purpose of the Study:
- To present a practical algorithm for maximum likelihood estimation (MLE) of MRF parameters.
- To demonstrate the algorithm's effectiveness on various MRF models and real-world image data.
Main Methods:
- Detailed theoretical framework for MLE of MRF parameters.
- Development and implementation of a computationally efficient algorithm.
- Application to standard Potts, inhomogeneous Potts, and long-range interaction models.
Main Results:
- Successful parameter estimation from synthetic and real images.
- Resynthesis of models to validate captured image features.
- Computation of image segmentations based on estimated parameters.
Conclusions:
- The developed algorithm provides a practical approach for MRF parameter estimation.
- The method effectively captures relevant features for image modeling and segmentation.
- This work advances the application of MRFs in image analysis.
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