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Their covariances are determined by maximising the likelihood of the density function via expectation-maximisation (EM).
This paper describes efficient techniques to identify and quantify variability in the parameters from experimental data by maximising the likelihood of the measurements, using the well-established Monte Carlo or perturbation methods for the likelihood computation.
Calibration of the model was conducted by maximising the likelihood value obtained by the sum of normal probability function of the residuals (mean = 0 and standard_deviation = (error variance)^(0.5)).
Furthermore, θ is a vector of eight parameters whose values we estimate by maximising the likelihood function, (7).
The parameter estimates are then obtained by maximising the likelihood function usually using the Expectation-Maximization algorithm or its variations.
The parameters β g = { β1, g, β2, g, β3, g, β4, g, β5, g } in the covariance function are estimated by maximising the likelihood.
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This is performed by maximising the marginal likelihood of the Gaussian process.
Then the probability of label C is estimated as: begin{aligned} P(C|v) = frac{1}{1+exp (As v +B)} end{aligned} (3)where A and B are two constants estimated by maximising the log likelihood of the training data (for details, see [31]).
Relative risk (RR) estimates were obtained by maximising the retrospective likelihood (RL) of observing the family genotypes conditional on all disease phenotypes.
For the ABC, UK and Manchester families, we adjusted for ascertainment by maximising the conditional likelihood of observing the phenotypes and genotypes in the families, given the disease status and age at diagnosis of the index case.
Risk estimates for the development of ovarian cancer were calculated as odds ratios (ORcrude) with 95% confidence interval (CI) using a conditional logistic regression model by maximising the conditional likelihood for all 127 case-control pairs.
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