Sentence examples for maximize a posterior from inspiring English sources

Every hyper-parameter is iterated by type-II maximum likelihood method to maximize a posterior.

The optimum values of lengthscale and nugget will be reached by looping over each x collecting approximate predictive equations to maximize a posterior.

Using the Monte Carlo technique, we detect vehicles sequentially based on the idea of maximizing a posterior probability (MAP), performing vehicle segmentation in the meantime.

Some estimation methods proceed by maximizing a posterior distribution or a likelihood function, and are amenable to an exact reconstruction of the optimal tree, but Bayesian phylogenetic analyses generally produce posterior distributions that are best explored by generating posterior samples.

Our decision is justified by the observation that, assuming independent random experiments, maximizing the posterior when deciding on a hypothesis in each will maximize the posterior over all choices because in this case the posterior over all choices is equal to the product of the individual choices.

From the Bayesian view point, MAP criterion is an estimation method that chooses an estimation of unknown variable which could maximize the posterior probability distribution function, i.e., θ ^ = arg max θ p θ | y (22).

When we have such a prior, we can use maximum a posteriory estimation (MAP) to find the parameters that maximize the posterior: The MAP estimate thus will balance between similarity of the parameters to the prior parameters η and the fit to the data.

The aim of the MAP algorithm is to maximize the posterior probability distribution for each transmitted symbol/codeword.

We want to maximize the posterior probability ({mathbb{P}}({mathcal{T}}_j | {mathcal{D}})) which is equivalent to maximizing ({mathbb{P}}({mathcal{D}}| {mathcal{T}}) cdot {mathbb{P}}({mathcal{T}})) over all possible models ({mathcal{T}}).

The linear discriminant analysis-maximum mutual information (LDA-MMI) method is used to maximize the posterior probabilities of all the belief score vectors [35], using objective function [36]: F MMI = ∑ ∀ i log p x i | P ( g ( i ) ) ∑ ∀ j p x i | λ j P ( j ), (28).

Findings that maximize the posterior probability of the correct hypothesis are the most valuable ones.