Sentence examples for inference section from inspiring English sources

For the Bayesian approach we develop a Markov chain Monte Carlo (MCMC) estimation method (see 'MCMC estimation for Bayesian inference' Section for details).

Hyperprior griddings are shown in the 'Hyperprior grids and hyperprior inference' section.

We then place a probability measure on ϕ z i 1, z i 2) in (15) and briefly consider prediction restricted to high confidence inference (Section 3.2) and strategies for removing possibly wrongly labelled datapoints in the training data (Section 3.3).

The first three rules of inference in Section 1.3.1 are called rules of λ-conversion.

On the other hand, following the model inference in Section 3.3, we continue to describe the MCMC sampling algorithm and the calculation of conditional posterior distributions for the remaining BGS-NMF hyperparameters {αr,βr,αh,βh,λr,λh,γr,δr,γh,δh}.

The inference framework (Sections 2.1 and 2.2.1), does not answer the question how to come up with a candidate network topology, which we would like to score.

Phased genotypes were then scored by the sum of the scores of their constituent haplotypes, and these values were summed up to score complete haplotype inferences (see Section "Implementation").

For example, A. alliaceus is usually observed basal to the other species in many phylogenetic inferences involving section Flavi [ 15], but these are usually based on analyses of one to three individual loci.

Aren't the methods of causal inference discussed in Section 2 sufficient as a methodology for experimental biology (see Rappaport 1996)?

Then, we compared the number of predicted repressors located on chromosome 19 with the number of predicted repressors located elsewhere (see Table 2 and section 'Inference of the preferential location of zinc-finger repressors of ERVs on chromosome 19 using Fisher's exact test with a noisy classifie r' in Supplementary Information).

The Bayesian inference is presented in Section 2. In Section 2, we describe implementation details of the proposed algorithm.