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  2. Markov's principle - Wikipedia

    en.wikipedia.org/wiki/Markov's_principle

    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 ...

  3. Markov theorem - Wikipedia

    en.wikipedia.org/wiki/Markov_theorem

    Download as PDF; Printable version; In other projects ... In mathematics the Markov theorem gives necessary and sufficient conditions for two braids to have closures ...

  4. Markov property - Wikipedia

    en.wikipedia.org/wiki/Markov_property

    The term Markov assumption is used to describe a model where the Markov property is assumed to hold, such as a hidden Markov model. A Markov random field extends this property to two or more dimensions or to random variables defined for an interconnected network of items. [1] An example of a model for such a field is the Ising model.

  5. Causal Markov condition - Wikipedia

    en.wikipedia.org/wiki/Causal_Markov_condition

    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.

  6. Effective topos - Wikipedia

    en.wikipedia.org/wiki/Effective_topos

    With this, one may validate Markov's principle and the extended Church's principle (and a second-order variant thereof), which come down to simple statement about object such as or (+). These imply C T 0 {\displaystyle {\mathrm {CT} }_{0}} and independence of premise I P 0 {\displaystyle {\mathrm {IP} }_{0}} .

  7. Markov model - Wikipedia

    en.wikipedia.org/wiki/Markov_model

    The simplest Markov model is the Markov chain.It models the state of a system with a random variable that changes through time. In this context, the Markov property indicates that the distribution for this variable depends only on the distribution of a previous state.

  8. Kolmogorov backward equations (diffusion) - Wikipedia

    en.wikipedia.org/wiki/Kolmogorov_backward...

    The Kolmogorov backward equation (KBE) (diffusion) and its adjoint sometimes known as the Kolmogorov forward equation (diffusion) are partial differential equations (PDE) that arise in the theory of continuous-time continuous-state Markov processes. Both were published by Andrey Kolmogorov in 1931. [1]

  9. Markov's inequality - Wikipedia

    en.wikipedia.org/wiki/Markov's_inequality

    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.