Sentence examples for inference describes from inspiring English sources

In contrast, reverse inference describes the probability for any particular BD (or PC) given activation in the anterior or posterior network.

Forward inference describes the probability of observing activity in a brain region given the knowledge of the psychological process, while reverse inference reflects the probability of a psychological process given the knowledge of activation in a specific brain region (cf. Poldrack 2006).

Such quantitative prior information as well as possible ranges for the parameter (f k × f u) were incorporated using Bayesian inference (described under "Statistical methods and models").

In this setting "complete-cluster" inference has been called inference for a hypothetical immortal cohort, and it has been suggested that "observed-cluster" inference (describing the population still alive at each timepoint) is of more interest (Dufouil et al., 2004).

For these experiments, we built tumor progression trees of the CC and BC data using the ploidyless heuristic approach to phylogenetic inference described in Methods. Figure 1 shows representative examples of tumor progression trees from the CC dataset.

Whilst certainly helpful, it is likely that the statistical inferences described earlier will never be better than direct cell counts, and any models incorporating measured or imputed cell counts may never be completely convincing, at least to referees.

That the rules for default inference also describe a scheme of approximate inference for all positive-valued conditional probabilities has also long been known.

For the core alignment, we used Bayesian inference (as described above) to infer a 50% majority-rule consensus phylogeny from the final 1000 trees in the posterior distribution.

Approximate algorithms are necessary for inference; in this work we employ the variational inference method described in Ref. [5].

Therefore, we apply KInfer to the network model we inferred with the time-lagged correlation based inference procedure described in the previous section.

To accomplish these goals, we introduce an improved statistical model of the insertion-deletion process to improve the accuracy of the inference, and describe a novel MCMC transition kernel to improve the speed of the inference.