Effects were termed positive, trivial or negative depending on the magnitude of the change relative to the SWC and assigned a qualitative descriptor according to the likelihood of the change exceeding the SWC: 50 74% 'possible', 75 94% 'likely', 95 99% 'very likely', >99% 'almost certainly'.
The remaining variables were successively incorporated according to the likelihood ratio of the changes (p < 0.05) in the model.
In order to detect speaker change points, speech segments were further segmented by means of a conventional metric-based approach[3] evaluating the likelihood of the acoustic change in the center of a sliding window using normalized Cross-BIC (XBIC) metric[27].
By combining current understanding of likelihood of the climate change hazard with knowledge of the sensitivity of a given catchment, it is possible to evaluate the fraction of climate model projections that would not be accommodated by specified safety margins.
In practice, to improve the rate of convergence of the Markov chains, we augmented the likelihood of the sound change model with that obtained from cognacy data for the same words, following methods described elsewhere [ 34, 35] (Supplemental Experimental Procedures).
This model is used to derive the maximum likelihood estimator of the change point, denoted by (hat{tau }).
The process is modeled and the maximum likelihood estimator of the change point is derived in "Process modeling and MLE derivation" section.
The maximum likelihood estimator of the change-point is the solution of (frac{d}{mathrm{d}x}log (phi )=0) with (mu), (sigma), and p replaced by maximum likelihood estimates.
The following discusses maximum likelihood estimation of the change point models, model comparison, the estimation of random effects, and the assessment of residuals.
However their drawback lies in the likelihood of neoplastic changes, if the proliferation and differentiation of the cells is not carefully controlled.
