As the true optimal treatment groups are hidden, model fits may be evaluated by measuring the congruity between observed treatment contrasts and predicted ones [ 34, 47].
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 speech model considered, words are hidden Markov models composed of concatenations of phonetic hidden Markov models.
HMMER implements methods using probabilistic models called profile hidden Markov models (profile HMMs).
Work presented in [38] summarized various statistical approaches for route prediction: Markov model (MM), hidden Markov model (HMM) and variable order Markov model (VMM).
This choice leads to MDMs with hidden states, formally, hidden Markov models (HMMs).
Several approaches have been proposed using different learning models, such as condition random fields (CRF), maximum entropy model (MEM), hidden markov model (HMM), and support vector machines (SVM).
Among the many new tools in use were Bayesian networks, hidden Markov models, information theory, stochastic modeling and classical optimization.
In many problems involving multivariate time series, hidden Markov models (HMMs) are often employed for modeling complex behavior over time.
The core ortholog database consisted of profile hidden Markov models of orthologous sequence groups from model vertebrates [ 32].
Motivation: Profile hidden Markov models (pHMMs) are currently the most popular modeling concept for protein families.
