125 research outputs found
Hidden Markov Model Identifiability via Tensors
The prevalence of hidden Markov models (HMMs) in various applications of
statistical signal processing and communications is a testament to the power
and flexibility of the model. In this paper, we link the identifiability
problem with tensor decomposition, in particular, the Canonical Polyadic
decomposition. Using recent results in deriving uniqueness conditions for
tensor decomposition, we are able to provide a necessary and sufficient
condition for the identification of the parameters of discrete time finite
alphabet HMMs. This result resolves a long standing open problem regarding the
derivation of a necessary and sufficient condition for uniquely identifying an
HMM. We then further extend recent preliminary work on the identification of
HMMs with multiple observers by deriving necessary and sufficient conditions
for identifiability in this setting.Comment: Accepted to ISIT 2013. 5 pages, no figure
Research Letter The Measurement Paradox in Valiant Network Design
Valiant network design was proposed, at least in part, to counter the difficulties in measuring network traffic matrices. However, in this paper we show that in a Valiant network design, the traffic matrix is in fact easy to measure, leading to a subtle paradox in the design strategy
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