On the other hand, Markov random field and conditional random field models have been well studied in fields of natural language processing [ 6, 7].
Both random effects and response values were simulated conditional on the parameter estimates, using 1000 samples.
These results pave the way for further research of the random metric theory and conditional convex risk measures.
To this end, we propose Weakly Supervised Sequence Modeling (WSSM), a novel framework that combines multiple-instance learning (MIL) and Conditional Random Field (CRF) model seamlessly.
The risk calculation that included fixed and random effects (the conditional risk calculation) showed the best overall discriminative ability in the development data (Table 2, c-index 0.69).
This integration is approximated by generating a large number of random ARGs conditional on the demographic model and averaging the likelihoods computed over this sample of ARGs.
30 This is a recommended procedure for missing data, 31 and assumes data are missing at random (MAR) conditional on the variables in the imputation model.
Examples include PBNs, Bayesian networks and their variants, e.g., dynamic and hierarchical Bayesian networks, hidden Markov models, factor graphs, Markov random fields, conditional random fields, Markov logic networks, etc.
Moreover, we assumed the missing mechanisms of the exposures and outcomes to be at random conditional on the covariates, and the measurement error of air pollution to be primarily Berkson (Zeger et al. 2000).
Instead, it assumes such mobility and matching to be approximately random, conditional on the very extensive set of person effects and time-varying characteristics of firms and workers (plus interactions) included in the estimated model.
