2016
NIPS
NeurIPS 2016
On Mixtures of Markov Chains
Abstract
We study the problem of reconstructing a mixture of Markov chains from the trajectories generated by random walks through the state space. Under mild non-degeneracy conditions, we show that we can uniquely reconstruct the underlying chains by only considering trajectories of length three, which represent triples of states. Our algorithm is spectral in nature, and is easy to implement.
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Interdisciplinary Bridge
— Data Science & Analytics and Machine Learning and Mathematics & Optimization
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Trend Setter
— Stochastic Processes
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Keyword Pioneer
— state reconstruction
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Hot Topic Early Bird
— markov chain
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Clustering
Machine Learning > Optimization & Theory > Stochastic Processes
Data Science & Analytics > Methods > Time Series Analysis
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Graphical Models
Mathematics & Optimization > Mathematics > Stochastic Processes
Mathematics & Optimization > Statistics