Sentence examples for accurate posterior from inspiring English sources

One of the algorithms used is Markov chain Monte Carlo (MCMC), which is capable of providing accurate posterior probability density (PPD) of the history-matched realizations.

Using this approach, it is possible to avoid reintroducing the noise in different saliency features, thereby obtaining a more accurate posterior probability.

Especially, to have accurate posterior estimates the simulation should be run in until the Monto carol error for each parameter of interest is less than about 5% of its posterior standard error, and in Bayesian Zero inflated Poisson Regression is accomplished.

In this study, precise and accurate posterior parameters should be derived from appropriate prior sensitivity of HRV test parameters.

The method takes as input a multiple sequence alignment, and outputs an accurate posterior probability of proximity for each residue pair.

The main bottlenecks are the cost of simulating large dynamical systems, and the need for large sample size in higher dimension for accurate posterior estimation.

We then evaluated the likelihood function at 5 000 × 5 000 grid spanning a large interval of λ(10-10 ≤ λ ≤ 30) and ϕ(10-10 ≤ ϕ ≤ 100) prior to normalization and marginalization in order to obtain accurate posteriors.

We proposed a method that gives accurate estimates, and a Bayesian alternative that permits full and exact posterior inference for any parameter or predictive quantity of interest.

If on the other hand the Markov chain sampler mixes poorly, then we can not rely on it to make accurate estimates of posterior probabilities.

A core task in machine learning, and the topic of this thesis, is developing faster and more accurate methods of posterior inference in probabilistic models.

The conclusions reached are: (1) the most accurate system is "posterior product", (2) multi-modal recognition has higher accuracy to either modality alone if all visual and tactile training data are pooled together, and (3) in the case of visual impairment, multi-modal recognition "learns faster", i.e. requires fewer training samples to achieve the same accuracy as either other modality.