Sentence examples for distribution likelihood from inspiring English sources

The noise model is considered as being defined by the parametric noise distribution "likelihood" (p imid theta _{i}^)), where (theta _{i}^) is an unknown space-varying parameter.

In general, Bayesian methods use the Bayes-Theorem to interpret the unknown estimate by converting outcomes of one or more trials of a prior probability distribution in a posterior distribution – likelihood estimation.

This approach exploits that our state-space model (11 - 14 11 - 14isden Markov model (cf. Figure 2), whidden11) iMarkov a state transition distribution f(x k |xk-1) and (14) leads to a modelrement distribution (likelihood funcfion) f(y k |x k ), which both are assumed known in the following.

Available IPD is used directly to inform this distribution (likelihood).

The Effective Sample Size (ESS) for the posterior distribution, likelihood and treeLikelihood showed satisfactory values, showing that the MCMC chain has been run for long enough to get a valid estimate of the parameter.

So, there you have it: poisson distributions, likelihood ratios and non-parametric rank tests.

The distribution of likelihood expected from the model is indicated with a solid line.

The dashed grey best-fit curves are calculated using the log-normal distribution maximum likelihood estimator.

with varying location parameter μ and shape parameter σ, estimated using the log-normal distribution maximum likelihood estimator method.

The spatial distribution of likelihood at the wave from N65°E (T1 in Fig. 4) and N30°W (T2) are shown in Fig. 5. Two vertical cross-sections indicate the distributions of the likelihood on the planes shown in Fig. 5.

Note that if the prior distribution and likelihood function conjugate each other, it will make the posterior distribution to have the same form with prior distribution [36].