Sentence examples for maximum likelihood baseline from inspiring English sources

The Bayesian methods based on posterior probabilities are consistently more accurate than the maximum likelihood baseline.

The results of the proposed method are statistically very significantly better than random, while the maximum likelihood baseline and the Inferelator are no better than random guessing.

The proposed Bayesian method based on posterior probabilities is clearly more accurate than the maximum likelihood baseline and also more accurate than the regression method in all cases.

The predictions of both these methods are significantly better than random (p < 0.01 or less in all cases using tail probability in a hypergeometric distribution) and clearly outperform the maximum likelihood baseline and the Inferelator.

According to the bootstrap testing, the proposed method is statistically significantly better than the alternatives in all cases except maximum likelihood baseline 200 top predictions (p < 0.01, except p < 0.05 for regression with 6003 top predictions; see Table 2 for full results).

Experiments performed on the Aurora noise-corrupted TIMIT database showed that the proposed approach provides meaningful performance improvement with an overall relative error reduction of 15.8% over the maximum likelihood-based baseline GMM approach.

The propensity score models the probability that a given patient would be exposed to the experimental treatment, conditionally to his(her) baseline covariates [ 1]: (3) log i t (P (Z = 1 | V ) ) = β ^ V where β ^ is the maximum likelihood estimator of the baseline covariate effects, and V is the vector of covariates included in the model.

First, using the restricted maximum likelihood estimator, all individuals with baseline data for the variables in the model (i.e., available case set) were included (ML analysis).

In Section 2, we outline the baseline CHMM with maximum likelihood and discriminative training.

We then compare the resulting separation performance with conventional maximum likelihood (ML) estimation and with two baseline approaches in an under-determined full-rank semi-informed scenario where the source positions and certain room characteristics are known.

Group averages are given in Table 2. *indicates missing data on these variables *Estimate from intention-to-treat ANCOVA controlling for baseline symptoms with maximum likelihood imputation of missing values.