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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 ...
A simple example of an NP-hard problem is the subset sum problem. Informally, if H is NP-hard, then it is at least as difficult to solve as the problems in NP. However, the opposite direction is not true: some problems are undecidable, and therefore even more difficult to solve than all problems in NP, but they are probably not NP-hard (unless ...
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 ...
Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations , probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms .
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.
Following similar logic as the conditional probability with direct calculation he finds the probability of picking two urns with white balls to be 1 / 3 . The earliest of several probability puzzles related to the Monty Hall problem is Bertrand's box paradox , posed by Joseph Bertrand in 1889 in his Calcul des probabilités . [ 65 ]
Performing a probabilistic risk assessment starts with a set of initiating events that change the state or configuration of the system. [3] An initiating event is an event that starts a reaction, such as the way a spark (initiating event) can start a fire that could lead to other events (intermediate events) such as a tree burning down, and then finally an outcome, for example, the burnt tree ...
The difference in the clueless host stems from the fact that in the original problem, it's a "clean" probability situation - you can consider all the options and draw out a standard probability tree. In the clueless situation, however, you need to arbitrarily lop branches off.