In other words, given the condition of neighboring chips, the remaining part of a wafer does not bear any additional information about the probability for a chip to fail. However, according to this model, the joint distribution of chips is known to involve a normalizing constant whose closed form can not be derived in general. Only the conditional distributions are available explicitely. This talk will explain the basic theory of Markov random fields and maximum pseudolikelihood estimation, and will also cover the practical aspects of model fitting, validation and comparison, as applied to microchips. A few open research areas will be indicated, from presumably straightforward extentions to simply stated world problems.
Research is fun.
That is why we do it. These are called mathematical models. These models are solved analytically and numerically. My work is done in close collaboration with experimental scientists who provide data on which to build the models and perform experiments to test hypotheses formed from studies with the models.