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The model can then be estimated by maximum likelihood and the transition probabilities can be calculated as a by-product of the estimation.
The parameters (phi ) and q may be estimated by maximum likelihood (ML), with the likelihood function constructed from the one-step ahead prediction errors.
In LDA, parameters can be estimated by Maximum Entropy, Variational Bayesian Inference [13], Expectation-Propagation [14], Gibbs sampling, etc. Gibbs sampling is a special case of Markov Chain Monte Carlo, it samples for a component of the joint distribution and keep the value of other components in a time.
If η is assumed to be the same for each associated SNP, then both α and η can be estimated by maximum likelihood, and a test of the null hypothesis can then be obtained as a likelihood ratio test.
Combining these data with our model, the model parameters can be estimated by maximum likelihood calculation.
For the jointly distributed time series { X t,y t }, t = 1,…n, the parameters of GAMAR can be estimated by maximum partial likelihood.
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The form of the covariance function and its parameters such as the maximum covariance, its characteristic length-scale and the intrinsic noise of the measurements can be estimated by maximum-likelihood or cross-validation, making it a very flexible technique for regression without strong a-priori-assumptions.
The parameters of the failure distribution and maintenance effect of equipment are estimated by maximum likelihood estimation.
Finally, the parameter values of naïve Bayes, i.e., mean and covariance of Gaussian distribution, are estimated by maximum likelihood estimators.
Subsequently, the parameters were estimated by maximum likelihood estimation.
The parameters are estimated by maximum likelihood estimation.
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