Search results
Results from the WOW.Com Content Network
To evaluate epistemic uncertainties, the efforts are made to understand the (lack of) knowledge of the system, process or mechanism. Epistemic uncertainty is generally understood through the lens of Bayesian probability, where probabilities are interpreted as indicating how certain a rational person could be regarding a specific claim.
Possibility theory is a mathematical theory for dealing with certain types of uncertainty and is an alternative to probability theory.It uses measures of possibility and necessity between 0 and 1, ranging from impossible to possible and unnecessary to necessary, respectively.
Arthur P. Dempster at the Workshop on Theory of Belief Functions (Brest, 1 April 2010).. The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory (DST), is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and imprecise probability theories.
Bayesian epistemology is a formal approach to various topics in epistemology that has its roots in Thomas Bayes' work in the field of probability theory. [1] One advantage of its formal method in contrast to traditional epistemology is that its concepts and theorems can be defined with a high degree of precision.
Historically, attempts to quantify probabilistic reasoning date back to antiquity. There was a particularly strong interest starting in the 12th century, with the work of the Scholastics, with the invention of the half-proof (so that two half-proofs are sufficient to prove guilt), the elucidation of moral certainty (sufficient certainty to act upon, but short of absolute certainty), the ...
The mutually exclusive event {5} has a probability of 1/6, and the event {1,2,3,4,5,6} has a probability of 1, that is, absolute certainty. When doing calculations using the outcomes of an experiment, it is necessary that all those elementary events have a number assigned to them.
The difference is how much the representation of the facts has been compressed by assuming that H is true. This is the evidence that the hypothesis H is true. If () is estimated from encoding length then the probability obtained will not be between 0 and 1. The value obtained is proportional to the probability, without being a good probability ...
The moment-independent sensitivity measures of , here denoted by , can be defined through an equation similar to variance-based indices replacing the conditional expectation with a distance, as = [(, |)], where (,) is a statistical distance [metric or divergence] between probability measures, and | are the marginal and conditional probability ...