Sentence examples for the observed realization from inspiring English sources

Let m be the observed realization of the (random) measurement vector.

The paper described the method for the transition intensity estimation from actual unit failures (deratings) and repair statistics, which was presented by the observed realization of generating capacity stochastic process.

Here, Y i is the observed realization of the function and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{ff} $$\end{document} f ^ is restricted to be a member of the class of twice-differentiable functions.

A covariance function estimate of a zero-mean nonstationary random process in discrete time is accomplished from one observed realization by weighting observations with a kernel function.

Moulton and Halsey [ 12] generalized the two-part model with logit/lognormal coupling by incorporating interval censoring, implying that the observed zeros were either a realization of the true zero point distribution or observations from the distribution of the positive outcome observed as zero due to detection limits.

Incorporating interval censoring in a zero-inflated mixture model implies that the observed zeros are either a realization of a 'true zero' point distribution, or an observation from the distribution of the positive outcome observed as zero due to detection limitation [ 12].

Realizations of the observed macroeconomic aggregates are explained in terms of unobserved equilibrium rates and unobserved transitory components.

Let X be the random variable whose realizations are the observed samples at the matched filter output and define the decision region associated to the information bit b i as begin{aligned} Z_{i}=left{ X in mathbb{R};~Prleft[widetilde{b}_{i} = b_{i}~|~ Xright] > Prleft[widetilde{b}_{i} neq b_{i} ~|~ Xright] right}.

The observation model determines the likelihood of the observed data (the patient swabs) for a given realization of the epidemic process (the augmented data), and the transmission and importation model specifies the likelihood of the realization given the model parameters.

You turn into the observed, rather than the observer.

Consider a Gaussian random field with a covariance function Σ and a realization vector x(s) =[x s1),…,x(s m )] T sampled at irregularly spaced locations s = [s1,…,s m ] T. The aim is to generate a set of extrema on a regular grid with the same mean and covariance structure as x(s) and to ensure that the realization passes through the observed values.