Effects of loss rate on ad hoc wireless routing

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 30-31).This thesis uses measurements from a deployed wireless ad hoc network to illustrate the effects of link loss rates on routing protocol performance. Measurements of this network show that the radio links between the majority of nodes have substantial loss rates. These loss rates are high enough to decrease forwarding performance, but not high enough to prevent existing ad hoc routing protocols from using the links. Link-level retransmission can mask high loss rates, at the cost of substantial decreases in throughput. Simulations, driven by the observed loss rates, show that the shortest paths chosen by existing routing protocols tend to find routes with much less capacity than is available along the best route.by Daniel Aguayo.M.Eng

Sequential Monte Carlo with transformations

This paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use in applications where the acceptance rate in reversible jump Markov chain Monte Carlo is low. We use this approach on model comparison for mixture models, and for inferring coalescent trees sequentially, as data arrives

Perturbation bounds for Monte Carlo within metropolis via restricted approximations

The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a perturbed Metropolis-Hastings (MH) algorithm, provides an approach for approximate sampling when the target distribution is intractable. Assuming the unperturbed Markov chain is geometrically ergodic, we show explicit estimates of the difference between the n-th step distributions of the perturbed MCwM and the unperturbed MH chains. These bounds are based on novel perturbation results for Markov chains which are of interest beyond the MCwM setting. To apply the bounds, we need to control the difference between the transition probabilities of the two chains and to verify stability of the perturbed chain. Keywords: Markov chain Monte Carlo, restricted approximation, Monte Carlo within Metropolis, intractable likelihood