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  2. Chain rule - Wikipedia

    en.wikipedia.org/wiki/Chain_rule

    The higher-dimensional chain rule can be proved using a technique similar to the second proof given above. [ 7 ] Because the total derivative is a linear transformation, the functions appearing in the formula can be rewritten as matrices.

  3. Chain rule (probability) - Wikipedia

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

    This rule allows one to express a joint probability in terms of only conditional probabilities. [4] The rule is notably used in the context of discrete stochastic processes and in applications, e.g. the study of Bayesian networks, which describe a probability distribution in terms of conditional probabilities.

  4. Hypothetical syllogism - Wikipedia

    en.wikipedia.org/wiki/Hypothetical_syllogism

    In propositional logic, hypothetical syllogism is the name of a valid rule of inference (often abbreviated HS and sometimes also called the chain argument, chain rule, or the principle of transitivity of implication). The rule may be stated:

  5. Faà di Bruno's formula - Wikipedia

    en.wikipedia.org/wiki/Faà_di_Bruno's_formula

    Faà di Bruno's formula is an identity in mathematics generalizing the chain rule to higher derivatives. It is named after Francesco Faà di Bruno (1855, 1857), although he was not the first to state or prove the formula.

  6. Jacobi's formula - Wikipedia

    en.wikipedia.org/wiki/Jacobi's_formula

    so that, by the chain rule, its differential is =. This summation is performed over all n×n elements of the matrix. To find ∂F/∂A ij consider that on the right hand side of Laplace's formula, the index i can be chosen at will. (In order to optimize calculations: Any other choice would eventually yield the same result, but it could be much ...

  7. Total derivative - Wikipedia

    en.wikipedia.org/wiki/Total_derivative

    The chain rule has a particularly elegant statement in terms of total derivatives. It says that, for two functions f {\displaystyle f} and g {\displaystyle g} , the total derivative of the composite function f ∘ g {\displaystyle f\circ g} at a {\displaystyle a} satisfies

  8. Conditional mutual information - Wikipedia

    en.wikipedia.org/wiki/Conditional_mutual_information

    The data processing inequality is closely related to conditional mutual information and can be proven using the chain rule. Interaction information The ...

  9. Power rule - Wikipedia

    en.wikipedia.org/wiki/Power_rule

    2.3.1 Proof by chain rule. 2.3.2 Proof by implicit differentiation. ... This proof is composed of two steps that involve the use of the chain rule for differentiation.