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In this paper we propose an approach for global vehicle localization that combines visual odometry with map information from OpenStreetMaps to provide robust and accurate estimates for the vehicle's position. The main contribution of this work comes from the incorporation of the map data as an additional cue into the observation model of a Monte Carlo(More)
Ancestral graphs (AGs) are graphical causal models that can represent uncertainty about the presence of latent confounders, and can be inferred from data. Here, we present an algo-rithmic framework for efficiently testing, constructing , and enumerating m-separators in AGs. Moreover, we present a new constructive criterion for covariate adjustment in(More)
Instrumental variables (IVs) are widely used to identify causal effects. For this purpose IVs have to be exogenous, i.e., causally unrelated to all variables in the model except the explanatory variable X. It can be hard to find such variables. A generalized IV method has been proposed that only requires exogeneity conditional on a set of covari-ates. This(More)
Instrumental Variables are a popular way to identify the direct causal effect of a random variable X on a variable Y. Often no single instrumental variable exists, although it is still possible to find a set of generalized instrumental variables (GIVs) and identify the causal effect of all these variables at once. Till now it was not known how to find GIVs(More)
Description A port of the web-based software 'DAGitty', available at <http://dagitty.net>, for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing(More)
During the analysis of packet log files from network experiments, the question arises which received packet belongs to which of the potentially many binary identical send events. We discuss this send-receive correlation problem for networks with local broadcast media. We can prove that assigning send and receive events is an NP-complete problem. However,(More)
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