Branch-and-Bound, Graphical Models, Structured Grids

Branch and bound pattern involves dividing the problem into smaller sub-problems, using the problem's definition to set constraints that must be met by sub-problems and eliminating the sub problems that do not satisfy the constraints. This is all done with the four steps: branching, evaluation, bounding and pruning. The theoretical aspects of the pattern are very well explained, but more code examples would have been helpful for understanding.

I did not understand exactly how the Graphical Models pattern solves the problem statement. In many problems we use measurements that can be performed to infer things and make conclusions. This is usually done with some kind of probability distribution as used in this pattern. But the solution is not clear to me. It is unfortunate that they did not include any examples to illustrate the solution.

I think the Structural Grids pattern is basically about using geometric decomposition to decompose the problem into smaller chunks and have processing elements operate on separate chunks in parallel. It looks like this is applicable to image processing, but I did not fully understand the example presented.