Abstract—We present an iterative joint scheduling-routing algorithm for characterizing the long-term performance of a cellular-relaying network. The physical layer model is based on ideal rate adaptation, fixed transmission power, and average interference. At the MAC layer, time-shares of a common

Hoc On-Demand Distance Vector Routing (AODV). A detailed simulation model with MAC and physical layer models is used to study interlayer interactions and their performance implications. We demonstrate that even though DSR and AODV share a similar on-demand behavior, the differences in the protocol

is available. This paper presents the results of a detailed packet-level simulation comparing four multi-hop wireless ad hoc network routing protocols that cover a range of design choices: DSDV, TORA, DSR, and AODV. We have extended the ns-2 network simulator to accurately model the MAC and physical

We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii

biological features of cellular systems. In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements. This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model

to thousands of nodes. By using a probabilistic bit error model for the network, TOSSIM remains simple and efficient, but expressive enough to capture a wide range of network interactions. Using TOSSIM, we have discovered several bugs in TinyOS, ranging from network bitlevel MAC interactions to queue overflows

We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer

. 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

A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences

, ignoring the correlations between them. Recently, Probabilistic Relational Models, a relational version of Bayesian networks, were used to define a joint probabilistic model for a collection of related entities. In this paper, we present an alternative framework that builds on (conditional) Markov networks