Sentence examples for model the hidden from inspiring English sources

In the Phys Chem ANN model the "hidden layers" are no longer virtual "neurons", but real structural units of proteins and peptides.

The central idea behind this project is that if we want to improve recognition performance through acoustic modeling, then we should first quantify how the current best model — the hidden Markov model (HMM) — fails to adequately model speech data and how these failures impact recognition accuracy.

In our HMM model, the hidden state is the occupation of H3K4me3 (or H3) nucleosome at a base pair, and the observed state is the genome sequence.

As in Gay et al.'s model, the hidden states X j ∈ {1,…, k} and G j jointly determine the index c j of the haplotype from which h k +1, j is copied.

In our model, the hidden states are the IBD number between the pair at each locus and the observable data are the numbers of alleles that are identical-by-state (IBS) between the pair.

These methods (identical for both the Scottish and the English prevalence estimates) model the "hidden population" of problem drug users not in contact with services from overlaps between populations of drug users known to a range of different services, including the drug treatment services where the DORIS sample was recruited.

Other modeling issues covered include: modeling of joint data (e.g., PKPD models), covariate models, mixture models, hidden Markov models, and stochastic differential equation-based models.

In the developed SaE-ELM model, the network hidden node parameters of the ELM are optimized using SaE algorithm.

In particular, we will first apply the hidden Markov model (HMM) to model spatio-temporal behavior patterns and use the expectation-maximization (EM) method to learn the parameters of the model.

They then used a well-known statistical method called the hidden Markov model, which was developed in the 1960s for speech recognition, to help them identify subtle patterns in the genomes of apes and humans.

The methods used were: (i) the neural network algorithm of SignalP v3.0 [51], (ii) the hidden Markov model of SignalP v3.0 [51], and (iii) the Sigcleave algorithm at EMBOSS [52].