Sentence examples for maximum likelihood integration from inspiring English sources

Unlike maximum likelihood integration (MLI), the Bayesian structure inference approach provides a systematic and quantitative solution to these kinds of problems.

In summary, the standard maximum likelihood integration approach to sensor fusion has dramatically failed to explain the experimental data in [17].

The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI) has been highly successful in modelling how the human brain combines information from a variety of different sensory modalities.

With this model, we are able to understand the results of experiments on human multisensory oddity detection [17] which the classical maximum likelihood integration theory, and other simpler theories for cue combination, fails to model with drastic qualitative discrepancy.

In [17], the standard maximum likelihood integration ideal observer approach failed with drastic qualitative discrepancy compared to human performance; however, this was due to simple maximum likelihood fusion being an inappropriate model rather than the failure of ideal observer modelling.

When the audio and visual stimuli were similar, a unified percept was reported and the reported position was approximately the weighted average of the stimulus as we might expect from maximum likelihood integration [15], [16].

Parameter estimation was obtained through maximum likelihood estimation with integral approximation via adaptive Gaussian quadrature with 15 integration points.

The expected average WTP according to the logit model, using maximum likelihood was calculated by numerical integration in the range of zero to the maximum amount of the proposed bid as follows: E WTP ={displaystyle underset{0}{overset{30000}{int }}}left(frac{1}{1+ ex{p}^{left{-left(2.134299-0.001845* BIDright)right}}}right dA=26820 (9).

When the summation is weighted in accordance to the variance associated with each of the initially independent sensory estimates, the combination process is often described as a Bayesian maximum likelihood estimate (MLE) or an optimal integration [7] [8].

We employed the robust maximum likelihood (MLR) estimator with Gauss-Hermite integration.

MRF, Markov random field; PPI, protein-protein interaction; RWR, random walk with restart; DIR, data integration rank; MLE, maximum likelihood estimation; MCMC, Markov chain Monte Carlo; OMIM, online Mendelian inheritance in man; PCC, Pearson correlation coefficient; ROC, receiver operating characteristic; AUC, area under the ROC curve.