Sentence examples for extended likelihood from inspiring English sources

In this paper, we propose an approach combining evolving population particle filtering with extended likelihood data association for MATT applications.

This research article investigates the feasibility of this extension, gives the extended likelihood function in closed form and proposes a slight approximation to decrease computational demand significantly.

The extended likelihood data association filters those measurements that belong to the target from a clutter of other noisy measurements analysed within the validation gate.

An algorithm with extended likelihood probabilistic data association and evolving groups of populations of particles representing a multiple-part distribution is designed.

We have proposed an innovative method for combining extended likelihood data association with evolving population particle filtering for robust and accurate multiple target tracking.

To account for the uncertainty in the origin of the measurement, the extended likelihood data association method [10] incorporates local attribute information of measurements weighted by probabilistic data association (PDA) for correctly identifying the measurement from the target as against the clutter.

We call this approach the extended likelihood-based reduction approach and compare its results to the regular likelihood-based reduction approach.

As can be seen from Table 9, the extended likelihood-based reduction approach yields an identical optimal model as the simple likelihood-based reduction approach although the path that both approaches take towards this optimal model is different (data not shown).

For each interval, the recombination rate is estimated by using an extended composite likelihood approximation of the coalescent likelihood [12], [30].

Since the post-order traversals (Algorithm 2) specifying the likelihood function are topology-specific, we extended the likelihood over a compact box of quartets in a topology-specific manner.

An extended maximum likelihood principle is described by which inverse solutions for problems with uncertainties in known model parameters can be treated.