Sentence examples for specification of the probability from inspiring English sources

The design process requires identifying the structure of the network, followed by specification of the probability parameters that operationalize these dependencies for the purpose of inference.

Quantification of the impacts of epistemic uncertainty is naturally difficult, because most of the existing stochastic tools rely on the specification of the probability distributions and thus do not readily apply to epistemic uncertainty.

We compare the benchmark specification of the linear probability model with a panel linear probability model with random effects, a panel linear probability model with fixed effects, and the marginal effects of a survival model.

It can be used to represent the dependence between random variables (features) and to give a concise and tractable specification of the joint probability distribution for a domain.

The likelihood depends necessarily on the sampling design and a complete specification of the joint probability function of the sample is usually not feasible under any without replacement sampling.

Both proposed designs require a prior specification of the model probabilities.

The empirical model of ionospheric irregularities based on ROTI data is actually for space weather studies and transionospheric radio propagation applications, i.e., estimation, specification, and prediction of the probability and area of ionospheric irregularity development and their dependence on solar and geomagnetic activity.

Thus, Bayesian inference requires specification not only of the probability distribution of the data, but also the probability distributions (priors) of model parameters.

Bayesian inversion requires the specification of prior probability distributions for model inputs and the calculation of the likelihood function that quantifies the ability of the model to reproduce the observed data (Mosegaard and Tarantola 1995; Kennedy and O'Hagan 2001).

After specification of the error term, the probabilities of observing values of y given x can be computed.

We applied a Bayesian methodology detailed in [ 24] which entails the specification of prior probability distributions for model parameters and these are listed in Table 1.