Bayesian Probability

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Bayesian Probability

A revision of a previous probability based on new information. In Bayesian analysis, one makes mathematical assumptions about unavailable information. As that information is gathered and disseminated, the Bayesian probability corrects or replaces the assumptions and alters its results accordingly.
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Whereas epistemic probability promises accurate foreknowledge through "quantitative knowledge of the probability of every possible outcome," aleatory uncertainty constitutes "neither entire ignorance nor complete and perfect information, but partial knowledge" [Knight, 1921, p.
Unlike previous arguments for the level specificity of chance, the present argument shows, in a precise sense, that higher level chance does not collapse into epistemic probability, despite higher level properties supervening on lower level ones.
Uncertainty in an individual element of y, say, the element j can be quantified by an epistemic probability distribution with the density [f.sub.j(y)][33].
Epistemic probability distributions assigned to elements of fire risk are specified and propagated though models of the multi-attribute selection by means of Monte Carlo simulation.
There is an important difference between giving each person the greatest equal epistemic probability of surviving and giving each the greatest equal chance of surviving.
In the framework of epistemic probability theories, two different security policies can be identified: In line with the "logical theory," security policies turn into a form of risk management.
The difficulty is supposed to be that such a belief must have a fairly high epistemic probability (lower than 1); and such probabilities are always, McGrew thinks, relative probabilities.
What I have in mind is the view that chance is just epistemic probability of a particular sort: consider two systems A and B, which differ with respect to the probability pertaining to A of some future event type (say a collapse into a state [Psi] at t).
This paper argues for a doctrine it calls "infallibilism," which is stipulated to mean that If S knows that p, then the epistemic probability of p for S is 1.
In this case, there is only one epistemic probability. Another is the possibility that each person has an epistemic probability measure, hence a theoretical state, which is the measure of that person's ignorance.
The sceptic rather argues a priori from the conceded logical possibility of some sceptical hypothesis, to the conclusion that we cannot justify the assignment of any epistemic probability to the denial of that hypothesis.
Once more, Alston takes a keen-edged scalpel to various central issues such as externalism versus internalism, reliabilism, foundationalism, coherence, truth-conduciveness, epistemic virtue, skepticism, contextualism (though Alston means something different by it than the view now widely disputed in the literature), and epistemic probability. However, his central thesis is that researchers should replace the notion of "justified belief" with a series of positive distinctively epistemic features of beliefs, called "epistemic desiderata" or "EDs." He maintains that philosophers have been bamboozled by focusing on the question of when a belief is justified.