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The idea is to have a function, which given a proposition and a world yields a set of propositions instead of a single proposition.
Given a proposition, a, that a horse is going to win the race, and various sources of evidence, h1, h2, h3…hn, one can estimate the probability of a given h1 to be p1, given h2 to be p2, and so on.
In probability theory the expression Pr(x|y)—called the conditional probability of x given a proposition y is customarily defined as Pr(x&y)/Pr y) provided that Pr y) > 0. (Hereafter whenever we use an expression with form Pr(…|y) we will always assume that Pr y) > 0).
In conclusion, given a proposition, languages differ in the time point at which the sentence mood is marked in the surface structure of interrogatives.
Similar(56)
Now we give a proposition, which will be applied to prove Theorem 1.1.
Let us give a proposition which is essential for our results.
Let us give a proposition relating to Dirichlet eta function first before we move forward to give another theorem.
Now, we give a proposition that extends a result due to Wall [3] in the case of scalar continued fractions.
Before proposing our algorithm, we give a proposition to show the equivalences between (P) and ((mathrm{P}_{rho })).
Also, we give a proposition which is the most important property for the proof of Theorem 1.1.
Now, we give a proposition for the maximal real roots of G n ( k ) ( x ) without proof.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com