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Markov's principle (also known as the Leningrad principle [1]), named after Andrey Markov Jr, is a conditional existence statement for which there are many equivalent formulations, as discussed below. The principle is logically valid classically, but not in intuitionistic constructive mathematics. However, many particular instances of it are ...
In the presence of Markov's principle, the syntactical restrictions may be somewhat loosened. [ 1 ] When considering the domain of all numbers (e.g. when taking ψ ( x ) {\displaystyle \psi (x)} to be the trivial x = x {\displaystyle x=x} ), the above reduces to the previous form of Church's thesis.
In probability theory, Markov's inequality gives an upper bound on the probability that a non-negative random variable is greater than or equal to some positive constant. Markov's inequality is tight in the sense that for each chosen positive constant, there exists a random variable such that the inequality is in fact an equality.
Download as PDF; Printable version; In other projects ... In mathematics the Markov theorem gives necessary and sufficient conditions for two braids to have closures ...
In theoretical computer science, a Markov algorithm is a string rewriting system that uses grammar-like rules to operate on strings of symbols. Markov algorithms have been shown to be Turing-complete , which means that they are suitable as a general model of computation and can represent any mathematical expression from its simple notation.
Markov chains and continuous-time Markov processes are useful in chemistry when physical systems closely approximate the Markov property. For example, imagine a large number n of molecules in solution in state A, each of which can undergo a chemical reaction to state B with a certain average rate.
A full team effort took place to get the Lions back in position to kick a game-winning field goal. The defense picked off C.J. Stroud twice and consistently put him under duress with the pass rush.
The related Causal Markov (CM) condition states that, conditional on the set of all its direct causes, a node is independent of all variables which are not effects or direct causes of that node. [3] In the event that the structure of a Bayesian network accurately depicts causality , the two conditions are equivalent.