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When calculating the cost of risk, three types of probabilities may exist: immediate, time independent, and time dependent.
The performance of a detection algorithm is, usually, characterized by two types of probabilities: the false alarm probability (P fa) and the detection probability (P d).
The orders of convergence to zero of both types of probabilities are assumed to be suitably bounded below according to an Osipov-type condition and the sample distribution is assumed to satisfy a corresponding tail condition due to Linnik.
The orders of convergence to zero of both types of probabilities are suitably bounded below by assuming an Osipov-type condition, see (Osipov 1975), and the sample distribution is assumed to satisfy a corresponding Linnik condition, see (Ibragimov and Linnik 1971; Linnik 1961).
Two types of probabilities need to be estimated: transition probabilities and emission probabilities.
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Two types of probability-boxes are distinguished: free and parametric (also called distributional) p-boxes.
Four types of probability density functions (PDFs) are used to model those uncertainties.
The solid and dotted curves denote the fit of two types of probability density functions (Laplace and Gaussian distributions).
There are three types of probability distributions used in this study: (1) log-normal; (2) beta; and (3) gamma.
The crucial observation is that there is a characterization of weak dominance in terms of best response to certain types of probability measures.
This allows the transition from initial qualitative models until the end of quantitative models, passing through intermediate stages of combination of both types of probability estimates.
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