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All the observation distributions are degenerate i.e. a single output can be observed in a state except the observation distribution for the Markovian state B with a mixture of primordia and emerged roots.
Broad and uniform observation distribution would be also favorable for good forecasting.
The model is characterised by the observation distribution, the transition distribution, and the initial state distribution.
The bottom panel of Fig. 3 shows the GPS observation distribution of Swarm-A and Swarm-C for the time period from 26 January to 3 March 2015.
Tables 1, 2, and 3 shows the initial state probabilities distribution, observation distribution, and transition probabilities respectively for each VM in the vCell.
Thus, the output of our algorithm may mistake the undesired objects for the desired one because of the inaccurate adopted observation distribution.
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As before, the basic models used are HMMs that employ Gaussian mixtures to represent the state observation distributions.
When there is a sufficient number of hidden states with a rich class of observation distributions, the HMMs can accurately represent probability distributions in complex real world problems and create simple and compact models [34].
In the settings of large numbers of variables and with few observation, distribution-free or Bayesian approaches by using useful prior information are more suitable in RNA-seq.
To a first approximation, the transformation applied to model data corrects the model distribution for bias and spread with respect to observation distributions.
For accurately estimating the location of the peak, some smoothing of the inter-observation distribution is necessary as the original distribution of data can fluctuate a lot, even though our data is already binned to 10 minute intervals due to the restricted temporal resolution of 10 minutes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com