the details of the posterior predictive approach in two problems: estimation in a model with inequality constraints on the parameters, and estimation in a mixture model. In all three examples, standard test statistics (either a χ 2 or a likelihood ratio) are not pivotal: the difficulty is not just how

in symbolic execution and dynamic test generation. Our approach records an actual run of the program under test on a well-formed input, symbolically evaluates the recorded trace, and gathers constraints on inputs capturing how the program uses these. The collected constraints are then negated one by one

present motivation and detailed technical description of the basic combinatorial optimization framework for image segmentation via s/t graph cuts. After the general concept of using binary graph cut algorithms for object segmentation was first proposed and tested in Boykov and Jolly (2001), this idea

on semantically typed data, tailoring content to the specific constraints of the client. We instantiate our design principles in a proxy architecture that further exploits typed data to enable application-level management of scarce network resources. Our proxy architecture generalizes previous work addressing all

and mental health both directly and as stress buffers, the theoretical mechanisms through which they do so still require elaboration and testing. New work suggests that coping flexibility and structural constraints on individuals' coping efforts may be important to pursue. Promising new directions

We describe the design and implementation of a finite domain constraint solver embedded in a Prolog system using an extended unification mechanism via attributed variables as a generic constraint interface. The solver is essentially a scheduler for indexicals, i.e. reactive functional rules

F ifteen years ago, Milton Friedman’s 1957 treatise A Theory of the ConsumptionFunction seemed badly dated. Dynamic optimization theory had not beenemployed much in economics when Friedman wrote, and utility theory was still comparatively primitive, so his statement of the “permanent income hypothe

parameterizations’ ’ and to prevent severe distortion of the Lagrangian surfaces. Deterministic baroclinic wave-growth tests and long-term integrations using the Held–Suarez forcing are presented. Impact of the monotonicity constraint is discussed. 1.

automates dynamic deadlock avoidance for conventional multithreaded programs. It employs whole-program static analysis to model programs, and Discrete Control Theory to synthesize lightweight, decentralized, highly concurrent logic that controls them at runtime. Gadara is safe, and can be applied to legacy

of each nucleotide base given a particular state. Machine learning techniques are applied to optimize these probabilities using a standardized training set. Given a new candidate sequence, the best parse is deduced from the model using a dynamic programlning algorithm to identify the path through