Abstract
Methods are discussed for the estimation of the parameters of a binary Markov random field (BMRF) defined on a graph. The standard method is maximum pseudo-likelihood (MPL) estimation. Maximum likelihood (ML) estimation has been hampered in the past by the intractability of the likelihood function. Recently Markov chain Monte Carlo (MCMC) methods have been introduced for ML estimation. In this paper a new method for Monte Carlo maximum likelihood is described. It is used for the estimation of the parameters of a simple model (the Ising model of statistical physics). As an application the distribution of fibre types in a cross-section of human muscle is analysed.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
