poordu to strong dependencies ofparameters in the posteriordistribuQ84K In this paper, we propose variou block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standardfu6 conditionals, and can be applied in amoduzK fashion in a large

. To deal with cells of various sizes, the algorithm progresses by moving one cell at a time between the blocks of the partition while maintaining a desired balance based on the size of the blocks rather than the number of cells per block. Efficient data structures are used to avoid unnecessary searching

changes to the basic structure of objects in the heap. However, these implementations perform poorly because they interpose on all accesses to shared memory in the atomic block, redirecting updates to a thread-private log which must be searched by reads in the block and later reconciled with the heap when

gutton~gte.com Dyna is an AI architecture that integrates learning, planning, and reactive execution. Learning methods are used in Dyna both for compiling planning results and for updating a model of the effects of the agent's actions on the world. Planning is incremental and can use

propose a novel block-oriented computation model, in which computation is iterated locally over blocks of highly connected nodes, significantly improving the amount of computation per cache miss. Following this model, we describe the design and implementation of a block-aware graph processing runtime

An important problem when using the Block Matching Algorithm to track objects is how to update the reference block to take account of the changing target appearance. This paper investigates and reports on the accuracy and stability of a variety of update strategies and on the robustness

Markov chain Monte Carlo methods are widely used to study highly structured stochastic systems. However when the system is subject to constraints, it is difficult to find irreducible proposal distributions. We suggest a "block-wise" approach for constrained sampling and optimisation

on the Mercury protocol can be used to construct a distributed object repository providing efficient and scalable object lookups and updates. By providing applications a range-based query language to express their subscriptions to object updates, Mercury considerably simplifies distributed state management. Our

and cons of a Bayesian block updating sampling scheme versus the least squares method in the context of a simple genetic mapping problem. Depending on the focus of analysis, we show that the McMC sampler does not always outperform the simpler approach from a frequentist perspective, and, more to the point

ofthis solution is that it is possible, in a cache-main memory subsystem, to delay updating the main memory until a block is needed in the cache (nonstore-through mode of operation). Index Terms-Caches, coherence, memory hierarchy, multi-processor systems, nonstore-through. I.