Network biology tools are valuable and highly instrumental in exploring previously hidden biological inferences particularly by resolving complex hormonal interplays in plants.
In this network, twenty-four nodes are hidden but our inference algorithm correctly inferred their states and relationships between the nodes in the network.
A number of powerful methods have used Bayesian inference or hidden Markov models (HMM) for estimating the number and location of recombination breakpoints while accounting for their uncertainty (Husmeier and McGuire 2003; Suchard, Weiss, et al. 2003; Minin et al. 2005; Bloomquist et al. 2009).
We are trying to infer hidden causes other people's thoughts from their behavior, but they are hidden inside other people's skulls, so the inference is very hard.
The issue was important because job bias is often hidden; proving it depends on inference and circumstantial evidence.
Comparison of similarity between groups of potential orthologs based on generating and then comparing hidden Markov models (HMMs) does allow inference of evolutionary distance.
However, even with multiple peaks supporting the inference, small covariate effects may be hidden under the between-sample variation.
To overcome hidden or spurious links in observed networks, some network inference may assist investigators to reconstruct a crime network.
Inference in LDA involves the estimation of hidden topics h and x given the words v for each document, i.e. p(h,x|V,α,W).
As a result, the network inference problem can be stated as how to uncover hidden (mathcal {E}) from known (mathcal {V}) and ({mathbf {t}}^c, c={1,2,ldots,M}), where M is the total number of cascades.
If we postulate hidden structure not present in scientific discourse to account for inductive inference, explanation, or other forms of reasoning, then we risk obscuring how scientists themselves access this structure to evaluate it (Woodward 2003: ch. 4).
