87,831 research outputs found
Breaking the habit: measuring and predicting departures from routine in individual human mobility
Researchers studying daily life mobility patterns have recently shown that humans are typically highly predictable in their movements. However, no existing work has examined the boundaries of this predictability, where human behaviour transitions temporarily from routine patterns to highly unpredictable states. To address this shortcoming, we tackle two interrelated challenges. First, we develop a novel information-theoretic metric, called instantaneous entropy, to analyse an individual’s mobility patterns and identify temporary departures from routine. Second, to predict such departures in the future, we propose the first Bayesian framework that explicitly models breaks from routine, showing that it outperforms current state-of-the-art predictor
Detailed plan for the COMET WP3 initial research activity - list of research projects and goals, participants and timing
Detailed plan for the COMET Work Package (WP) 3
Dynamic Bayesian Combination of Multiple Imperfect Classifiers
Classifier combination methods need to make best use of the outputs of
multiple, imperfect classifiers to enable higher accuracy classifications. In
many situations, such as when human decisions need to be combined, the base
decisions can vary enormously in reliability. A Bayesian approach to such
uncertain combination allows us to infer the differences in performance between
individuals and to incorporate any available prior knowledge about their
abilities when training data is sparse. In this paper we explore Bayesian
classifier combination, using the computationally efficient framework of
variational Bayesian inference. We apply the approach to real data from a large
citizen science project, Galaxy Zoo Supernovae, and show that our method far
outperforms other established approaches to imperfect decision combination. We
go on to analyse the putative community structure of the decision makers, based
on their inferred decision making strategies, and show that natural groupings
are formed. Finally we present a dynamic Bayesian classifier combination
approach and investigate the changes in base classifier performance over time.Comment: 35 pages, 12 figure
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