. For each experimental run, we first gen erated random CPTs. We then sampled from the joint distribution defined by the network and clamped the observed nodes (all nodes in the bottom layer) to their sampled value. Given a structure and observations, we then ran three inference algorithms -junction tree

Abstract — In this paper, we set forth a new algorithm for generating approximately uniformly random spanning trees in undirected graphs. We show how to sample from a distribution that is within a multiplicative (1 + δ) of uniform in expected time e O(m √ n log 1/δ). This improves the sparse graph

on the Frobenius and the spectral matrix norms. We describe several novel (deterministic and randomized) approximation algorithms for this problem with approximation bounds that are optimal up to constant factors. Additionally, we show that the combinatorial problem of finding a low-stretch spanning tree

latency. We study these mechanisms in an implementation called Collection Tree Protocol (CTP) and evaluate their contributions to its performance. We evaluate CTP on 12 different testbeds ranging in size from 20–310 nodes and comprising 7 hardware platforms, on 6 different link layers, and on interference

. At necropsy, the ovoid mammary tumors were well encapsulated and easily excised. Mean (±SEM) mammary tumor size across all females was 78.4 ± 15.6 g, approximately 20% of the mean aged female body weight (i.e. approximately 400 g). Random samples revealed that they were histologically complex, with multiple

We propose a combination of techniques that solve multiple queries for motion planning problems with single query planners. Our implementation uses a probabilistic roadmap method (PRM) with bidirectional rapidly exploring random trees (BI-RRT) as the local planner. With small modifications

to be correlated. Generating random samples from such a distribution presents a computational challenge due to the dense matrix-vector multiplication needed to introduce the required correlations. This paper proposes a hardware architecture for generating random vectors, utilising the embedded block RAMs

-but there is no simple way for the standard model to explain the large span differences found at matched articulation rates. Thus, the span advantage seen for words over nonwords, high-frequency over lowfrequency words, or concrete over abstract words must be attributable to factors other than those contained

statement which has had one of the most profound impacts on industrial development of digital signal processing (DSP) systems. Over the years, theory and practice in the field of sampling have developed in parallel routes. Contributions by many research groups suggest a multitude of methods, other than

is particularly important for matrix-oriented programming languages such as R, where matrix/vector calculations tend to be faster than scalar operations implemented in a loop. We illustrate the application of the vectorized implementation in real and simulated data sets, when bootstrapping Pearson’s sample