Sentence examples for maximum posterior mode from inspiring English sources

If H k <1, the delay/Doppler bin containing the detected target, , is obtained by applying the maximum posterior mode (MPM) criterion.

The expectation-maximization algorithm was applied to estimate SNP effects β j by finding the maximum posterior mode, which treats the polygenic effect (g) as a missing variable (see Additional file 2 for details).

It can produce maximum likelihood and posterior mode estimates for model parameters when given only the emission probabilities to work with.

Then, for the fixed maximizing hyper-parameters, the maximized solution of the penalized log-likelihood in (3) is nothing but the optimal maximum posterior estimate, i.e., the mode of the posterior density.

Estimation using the posterior mode is known as maximum a posteriori (MAP) estimation.

The MAP terminology originates from Bayesian inference, where the posterior mode is equal to the maximum likelihood estimate when the prior density is vague and uniform, see, e.g., Rabe-Hesketh and Skrondal (2009) who discuss the estimation of random effects in a generalised linear mixed-effects model.

The likelihood function is defined on Ω, and the relationship R in Ω that produces the maximum value of the likelihood is the maximum likelihood estimate of the true relationship, corresponding to the posterior mode with a uniform prior.

We augment each plot with the maximum a posteriori (MAP) estimate (i.e. the posterior mode) of the entire recording probability function, estimated by the location of the highest marginal posterior probability (after integrating out λ) in the chains.

For this pixel, the bin index for the maximum cross-correlation does not equal the one for the posterior mode.

Once the posterior probability density function is obtained, a minimum mean square error estimation and maximum posterior probability density estimation can be accomplished by compute mean and mode, respectively.

We use a single chain and non-informative prior for all imputations, and expectation-maximization (EM) algorithm to find maximum likelihood estimates in parametric models for incomplete data and derive parameter estimates from a posterior mode.