The phrase "accurate likelihood" is correct and usable in written English.
It can be used when discussing the probability or chance of an event occurring with precision or correctness.
Example: "The accurate likelihood of rain tomorrow is based on the latest meteorological data."
Alternatives: "precise probability" or "exact chance".
Moreover, it is shown that the elliptical approximation of confidence regions of nonlinear model parameters can be very poor sometimes and that more accurate likelihood confidence regions can be constructed with PSO, allowing for more reliable statistical analysis of the significance of parameter estimates.
This results in more accurate likelihood ratios, which can be used directly for soft-decoding or to determine better decision regions for hard-decoding.
By comparison, the application of the SV model has been limited for a long time because of its shortcomings in parameter estimation and in the accurate likelihood function.
ATS 3 was the most accurate (likelihood ratio positive, 2.45, positive predictive value 0.73) and ATS 2 the most valid (area under the curve 0.567) category.
Alternative approaches that model the missing alignment data probabilistically or by imputation would allow more accurate (likelihood or Bayesian) phylogenetic techniques to be applied while still retaining all the input sequences.
She added, "If the public believes it's accurate, the likelihood is that because it's a low-key ad, the public is likely to accept it as legitimate campaigning".
SNP-HWE also recognizes that it is unnecessary to start the computation with an accurate absolute likelihood for one table.
Although our results show that all tests are reasonably accurate, the likelihood ratios suggest that neither one of them is suitable to rule out or rule in the presence of disease [ 19].
Based on this reformulation of the problem and subsequent derivation of an accurate log-likelihood function for the received pilot tones, we derive in detail the CRB for the joint parameter estimation problem under consideration.
While this is useful, when classification models are not 100% accurate, the likelihoods of correctness for the class predictions (i.e., classification ranking) can be useful for further research (e.g., deriving inferences for predictor genes and prioritizing experiments).
However, we feel this gives a roughly accurate estimate of likelihood.
