•Markov chains, HMMs have 1D structure
–At every time, there is one state.
–This enabled use of dynamic programming.
•Markov Random Fields break this 1D structure.
–Field of sites, each of which has a label,
simultaneously.
–Label at one site dependent on others, no 1D structure
to dependencies.
–This means no optimal, efficient algorithms.
•Why MRFs?
Objects have parts with complex dependencies. We need to model these. MRFs (and belief nets) model complex dependencies.