These models are additive and assume that all effects are Gaussian to utilize Gibbs sampling for parameter estimation.
Specifically, our multichannel parameter estimation approach is similar to that proposed in [16, 17] for Gaussian parametric model-based STAP detectors, whereas we utilize only training signals for parameter estimation.
It decides cluster allocation via a Gaussian Mixture Model utilizing an EM algorithm for parameter estimation and decides the optimal number of clusters via the Bayesian Information Criterion BICC).
By incorporating prior biological knowledge, utilizing advanced statistical method for parameter estimation and modeling unobserved nodes as latent variables, we developed a novel approach to infer active signaling networks from experimental data and a canonical network.
Patlak linear regression method was applied for parameter estimation utilizing the IDIF, [18F]-FDG tissue TACs [16], and PMOD version 3.5 (PMOD Technologies).
Differentially expressed genes between brainstem and cerebral cortex glioma samples were identified using the limma [ 47] package in Bioconductor, which utilizes an empirical Bayes' approach for parameter estimation and significance testing.
Alternative to the UKF, in this report, we proposed to use the EKF for parameter estimation.
The latter require sophisticated numerical integration routines, approximate likelihood techniques, or Bayesian approaches that utilize Markov Chain Monte Carlo (MCMC) methods for parameter estimation.
Here, we utilize both melting experiments and PDB data for parameter estimation.
Further, compared to multiple model based adaptive FTC design, the proposed methodology circumvents the issues of stability due to switching between different models and utilizes a minimum number of estimation models for parameter estimation without sacrificing the output performance and thereby reducing the computational burden.
