Sentence examples for likelihood theory from inspiring English sources

That's why you calculate your adjusted figures using cleverer methods, such as logistic regression, and likelihood theory.

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.

Along with likelihood theory (also associated with Fisher), the general frequentist paradigm views parameters (such as population means, variances, and regression coefficients) as unknown and fixed.

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).

Because of this lack of prior knowledge, standard likelihood theory or extensions such as generalized likelihood ratios or invariance-based approaches do not apply.

Inference in DHGLM is based on the h-likelihood theory and is a direct extension of the hierarchical GLM (HGLM) algorithm [ 11].

H-likelihood theory is based on the principle that HGLMs consist of three objects: data, fixed unknown constants (parameters) and unobserved random variables (unobservables).

Here we present the h-likelihood theory for DHGLM and refer to the paper on DHGLM by Lee & Nelder [ 10] for further details.

Here we summarize the h-likelihood theory for HGLM according to the original paper by Lee & Nelder [ 11], which justifies the estimation procedure and inference for HGLM.

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.