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A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram.
Although inference for the PLP is generally based on maximum likelihood theory, some advantages of the Bayesian approach have been reported in the literature.
Most straightforward from maximum likelihood theory is the Unconditional Maximum Likelihood approach (UML; or Joint Maximum Likelihood, JML; cf. Baker and Kim 2004, ch. 5.6).
The maximum likelihood theory
Models were fitted under maximum likelihood theory by using the statistical package Mendel///version 3.2 [ 26].
We could compare our results with estimates based on maximum likelihood theory.
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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.
Next, considering the maximum likelihood estimation theory, a user behavior influence model (EIPR) of a hotspot topic is proposed to measure the causes of user behavior behind the social hotspots.
Estimation of the parameters of the SAR model using Maximum Likelihood (ML) theory is computationally very expensive because of the need to compute the logarithm of the determinant (log-det) of a large matrix in the log-likelihood function.
Among them, the most widely used and advanced theory is the maximum likelihood estimation (MLE) theory for the location-scale distribution and the linear stress-life relationship.
Bayesian analysis; maximum likelihood, large sample theory.
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