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  2. Conditional probability - Wikipedia

    en.wikipedia.org/wiki/Conditional_probability

    P(A|B) may or may not be equal to P(A), i.e., the unconditional probability or absolute probability of A. If P(A|B) = P(A), then events A and B are said to be independent: in such a case, knowledge about either event does not alter the likelihood of each other. P(A|B) (the conditional probability of A given B) typically differs from P(B|A).

  3. Bayes' theorem - Wikipedia

    en.wikipedia.org/wiki/Bayes'_theorem

    P(A) is the proportion of outcomes with property A (the prior) and P(B) is the proportion with property B. P(B | A) is the proportion of outcomes with property B out of outcomes with property A, and P(A | B) is the proportion of those with A out of those with B (the posterior). The role of Bayes' theorem can be shown with tree diagrams.

  4. Law of total probability - Wikipedia

    en.wikipedia.org/wiki/Law_of_total_probability

    In probability theory, the law (or formula) of total probability is a fundamental rule relating marginal probabilities to conditional probabilities. It expresses the total probability of an outcome which can be realized via several distinct events , hence the name.

  5. Conditional independence - Wikipedia

    en.wikipedia.org/wiki/Conditional_independence

    In probability theory, conditional independence describes situations wherein an observation is irrelevant or redundant when evaluating the certainty of a hypothesis. . Conditional independence is usually formulated in terms of conditional probability, as a special case where the probability of the hypothesis given the uninformative observation is equal to the probability

  6. Chain rule (probability) - Wikipedia

    en.wikipedia.org/wiki/Chain_rule_(probability)

    In probability theory, the chain rule [1] (also called the general product rule [2] [3]) describes how to calculate the probability of the intersection of, not necessarily independent, events or the joint distribution of random variables respectively, using conditional probabilities.

  7. Probability - Wikipedia

    en.wikipedia.org/wiki/Probability

    The opposite or complement of an event A is the event [not A] (that is, the event of A not occurring), often denoted as ′,, ¯,,, or ; its probability is given by P(not A) = 1 − P(A). [31] As an example, the chance of not rolling a six on a six-sided die is 1 – (chance of rolling a six) = 1 − ⁠ 1 / 6 ⁠ = ⁠ 5 / 6 ⁠ .

  8. Boolean satisfiability problem - Wikipedia

    en.wikipedia.org/wiki/Boolean_satisfiability_problem

    Since a XOR b XOR c evaluates to TRUE if and only if exactly 1 or 3 members of {a,b,c} are TRUE, each solution of the 1-in-3-SAT problem for a given CNF formula is also a solution of the XOR-3-SAT problem, and in turn each solution of XOR-3-SAT is a solution of 3-SAT; see the picture. As a consequence, for each CNF formula, it is possible to ...

  9. Davis–Putnam algorithm - Wikipedia

    en.wikipedia.org/wiki/Davis–Putnam_algorithm

    Right: Resolving the given formula on , then on , then on yields the empty clause; hence the algorithm returns "unsatisfiable". The procedure is based on Herbrand's theorem , which implies that an unsatisfiable formula has an unsatisfiable ground instance , and on the fact that a formula is valid if and only if its negation is unsatisfiable.