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Prophase onset coincide with circadian slowdown.tex  figures/pro_met_div3/pro_met_div.png  Discussion.tex  subsection_hidden_Markov_models_for__.tex  Methods.tex  Hidden Markov Models.tex  Waveform optimization.tex  diff --git a/subsection_hidden_Markov_models_for__.tex b/subsection_hidden_Markov_models_for__.tex  deleted file mode 100644  index 89dc2f0..0000000  --- a/subsection_hidden_Markov_models_for__.tex  +++ /dev/null 
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\subsection{hidden Markov models for phase inference}  In this work we developed a novel method (TODO:true?) of inferring phases from data using hidden Markov models. We found that this method is adequate to decode real-world data, as it allows to specify a noise model as well as the underlying stochastic dynamics, and to explicitly take into account features of the data like amplitude, making the hypothesis underlying the analysis more manifest. A consequence of this is that several parameters have to be calibrated. Note however that because we learn the waveform, linking the phase to the data, from a large collection of time traces, our inferred phase is robust to transformations of the data, unlike methods based on the Hilbert transform \cite{Kralemann_2008}.