Sentence examples for likelihood space from inspiring English sources

The Metropolis-Hasting (M-H) algorithm is usually used to explore the model likelihood space for NSE.

To reduce the computational burden of this experiment, branches with equivalent simulated lengths were assumed to be equal, reducing the dimensionality of the likelihood space.

Three independent runs of codeml were made with different initial estimates for the transition/transversion rate ratio (k) and the dN/dS ratio (run 1: k = 1, dN/dS = 1; run 2: k = 0.1, dN/dS = 10; run 3: k = 10, dN/dS = 0.1) so that each run began at a different point in the likelihood space.

Following references [ 19] and [ 20], we transform (5) into a one-dimensional integral over likelihood space.

The relative frequencies of these HC calls look similar to the general relative likelihood space of the models.

Simulations have shown that accurate estimation is difficult for small values due to a flat ridge in the likelihood space in that region.

However, they could be used to validate our similarity (and penalty) measures in the log-likelihood space.

In this work, we have proposed a novel ensemble HMM classification method that is based on clustering sequences in the log-likelihood space.

The proposed approach, called ensemble HMM (eHMM), is based on clustering within the log-likelihood space and has two main steps.

The proposed approach, called ensemble HMM (eHMM), attempts to partition the training data within the log-likelihood space and identify multiple clusters in an unsupervised manner.

In fact, the estimates for λ decrease as m increases yet the corresponding log-likelihood value decreases, thus providing a sense of the contour of the larger log-likelihood space that is determined by λ,ν, and m.