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Formally, we add to a basic finite probability model a relation R ⊆ W2.
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For the first time, the experimental distributions are fitted by a theoretical probability model, accounting for the finite size of the crystals.
In a stochastic model, introduction of 1 infected person has a finite probability of resulting in the rapid extinction of an infectious disease.
Second, according to our newly-established transcriptome-sampling model [ 37], transcripts with certain expression levels have finite probability to be detected at a given sampling stage.
The numerical model of finite elements is based on the continuum strong discontinuity approach for composite materials and the Weibull probability model for the failure progressive of fibers.
We consider a probability model where the design-based approach to inference under simple random sampling of a finite population encompasses a simple random permutation super-population model.
The next parametric probability model under consideration is the discrete multinomial model which takes probability mass only on a finite number of points on the interval (0,1).
However, for certain changes in the quantum numbers, summarized as selection rules, there is a finite probability.
The problem is that multiplying ∞ by any positive, finite probability again yields ∞.
The same basic language as was used for the basic finite probability logic need not change, but the semantics is slightly different: for every state w ∈ W, the component Pw of a modal probabilistic model is replaced by an entire probability space (Ωw, Aw, μw), such that Ωw ⊆ W and Aw is a σ-algebra over Ωw.
Linear probability model.
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