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While in deterministic systems optimisation based approaches have been shown to yield some success [ 8, 9], the problem in stochastic systems is compounded by the fact that the true state of the system is also a random variable, and its distribution must be inferred (the so-called state inference problem).
From these, we would like to infer the true continuous time trajectory of the system (state inference problem) and estimate the parameters of the model.
We control for many parameters and other choices, such as the state inference scheme used.
The system is composed of a signal processing module and a state inference module.
The procedure for estimating the latent state sequence is summarized in Fig. 3. Fig. 3 The DFHMM state inference scheme.
Oztop et al. [22] developed a computational model of mental-state inference that used the circuitry that underlay motor control.
where p(o k |s k,z1 k) is state inference; z1 kis the set of measurements for time t = 1 to k; p(s k |z1 k) is the mode learning.
Lastly, the temporal probability distribution, derived from the word errors and the state inference, provides us with a method to assign weights to parts of the decoded sequence.
On the basis of thus obtained order features, the potential fault types are then deduced with the aid of a state inference engine.
The reason is that, in the proposed algorithm, the sufficient statistics for updating θ and the mobile state inference for x k are largely dependent on the density estimation of s k.
This was expected since it was the only method with some justification in our particular case, as it arises out of constraining the full state inference problem on the mixture.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com