Sentence examples for problems of inference from inspiring English sources

This scenario is appraised here with some illustrative examples, and with some interesting problems of inference on stochastic ordering (dominance) in parametric as well as beyond parametric setups.

The chapter also contains discussions of the kinds of consecution or consequence, problems of inference connected with the referents of terms used in consecutive sentences, and also on how to contradict a conditional sentence.

Case studies of a particular change in the minimum wage in a particular industry typically used only a short time horizon (raising obvious concerns about missing lags in disemployment effects), and in covering individual cases raised problems of inference and external validity.

The MML criterion provides an information theoretic objective for problems of inference where the goal is to find the best explanation (or theory, hypothesis, model) for a set of observed data (Wallace and Boulton, 1968).

To overcome the problems of inference resulting from separate consideration of effects of gene flow and adaptation as well as from artificial delineation of stands or units in experimental studies, the integrated approach to population delineation suggested by Kleinschmit et al. [ 1] will be further developed in this paper.

The key aspect of the problem of inference which is an inverse problem, that is, to infer value parameters based on measurements, resides on being able to make explicit any available a priori information about the system being modeled.

We argue that the problem of plan recognition, inferring an agent's plan from observations, is largely a problem of inference under conditions of uncertainty.

The following paper addresses a problem of inference in financial engineering, namely, online time-varying volatility estimation.

We find that the IV-GMM technique is a useful approach for the emergent problem of inference in hybrid simulation-statistical transportation datasets, due to fewer assumptions on distributional assumptions about the data, while accounting for statistical effects relating to endogeneity, potential selection effects and heteroscedasticity.

A good spatial analogy for our proposed computational approach is the problem of inference of a three-dimensional shape (which corresponds to the joint distribution of arc and node variables) of an object, starting with its orthogonal projections (which correspond to the conditional distributions of arcs given nodes and nodes given arcs).

Variational Bayesian inference aims to repose the problem of inference as an optimization problem rather than a sampling problem.