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In probability theory, a tree diagram may be used to represent a probability space. A tree diagram may represent a series of independent events (such as a set of coin flips) or conditional probabilities (such as drawing cards from a deck, without replacing the cards). [1] Each node on the diagram represents an event and is associated with the ...
Tree diagram (probability theory), a diagram to represent a probability space in probability theory; Decision tree, a decision support tool that uses a tree-like graph or model of decisions and their possible consequences; Event tree, inductive analytical diagram in which an event is analyzed using Boolean logic; Game tree, a tree diagram used ...
The overall goal of event tree analysis is to determine the probability of possible negative outcomes that can cause harm and result from the chosen initiating event. It is necessary to use detailed information about a system to understand intermediate events, accident scenarios, and initiating events to construct the event tree diagram.
Probability is the branch of mathematics and statistics concerning events and numerical descriptions of how likely they are to occur. The probability of an event is a number between 0 and 1; the larger the probability, the more likely an event is to occur. [note 1] [1] [2] This number is often expressed as a percentage (%), ranging from 0% to ...
Tree diagram; Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability ...
Tree diagram; The standard probability axioms are the foundations of probability theory introduced by Russian mathematician Andrey Kolmogorov in 1933. [1]
English: Tree diagram for the probabilities of events A and B. Date: 6 October 2012, 22:13:38: Source: Own work: ... Tree diagram (probability theory) Global file usage.
The left tree is the decision tree we obtain from using information gain to split the nodes and the right tree is what we obtain from using the phi function to split the nodes. The resulting tree from using information gain to split the nodes. Now assume the classification results from both trees are given using a confusion matrix.