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In this case, it makes perfect sense to nest such statements of objective conditional probability within Boolean operators (either the probability of q on p is close to 1, or the probability of r on s is close to 1), or within operators of objective probability (the objective probability that the objective probability of p is close to 1 is itself close to 1).
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By optimizing the above weighted objective, the conditional probabilities of words with large weights will have more proportions to the entire objective.
The latter is determined by a simple set-up procedure based on calculations of the objective function's conditional expectations for uncertain parameters.
The central basic point and lower bounds are obtained through a set-up procedure which relies on a one-dimensional calculation of the objective function's conditional expectations for each uncertain parameter.
This risk-averse model features a mean-risk objective, where the conditional value-at-risk (CVaR) is specified as the risk measure, and enforces a joint probabilistic constraint on the feasibility of the second-stage problem concerned with distributing the relief supplies to the affected areas in case of a disaster.
We hypothesised also that this difference in matched scores would predict objective progress including conditional discharge.
Our ultimate objective is the conditional probability distribution of the degree.
Based on previous work, we hypothesised that self-rating scores might be predictors of objective progress including conditional discharge.
The SLA@SOI model consists of terms, service level objectives (SLO) and conditional rules.
Our objective is to estimate conditional minimum detectable changes (cMDCs).
However, there is no reason why the logic of extreme probabilities couldn't be given a different interpretation, with (p ⇒ q) representing something like the objective probability of q, conditional on p, is infinitely close to 1.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com