CLUSTERING IN BLOCK MARKOV CHAINS

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This paper considers cluster detection in Block Markov Chains (BMCs). These Markov chains are characterized by a block structure in their transition matrix. More precisely, the n possible states are divided into a finite number of K groups or clusters, such that states in the same cluster exhibit the same transition rates to other states. One observes a trajectory of the Markov chain, and the objective is to recover, from this observation only, the (initially unknown) clusters. In this paper, we devise a clustering procedure that accurately, efficiently and provably detects the clusters. We first derive a fundamental information-theoretical lower bound on the detection error rate satisfied under any clustering algorithm. This bound identifies the parameters of the BMC, and trajectory lengths, for which it is possible to accurately detect the clusters. We next develop two clustering algorithms that can together accurately recover the cluster structure from the shortest possible trajectories, whenever the parameters allow detection. These algorithms thus reach the fundamental detectability limit, and are optimal in that sense.
Publisher
INST MATHEMATICAL STATISTICS
Issue Date
2020-12
Language
English
Article Type
Article
Citation

ANNALS OF STATISTICS, v.48, no.6, pp.3488 - 3512

ISSN
0090-5364
DOI
10.1214/19-AOS1939
URI
http://hdl.handle.net/10203/279437
Appears in Collection
AI-Journal Papers(저널논문)
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