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This theorem has since been extended to the time-dependent domain to develop time-dependent density functional theory (TDDFT), which can be used to describe excited states. The second HK theorem defines an energy functional for the system and proves that the ground-state electron density minimizes this energy functional.
The hockey stick identity confirms, for example: for n=6, r=2: 1+3+6+10+15=35. In combinatorics , the hockey-stick identity , [ 1 ] Christmas stocking identity , [ 2 ] boomerang identity , Fermat's identity or Chu's Theorem , [ 3 ] states that if n ≥ r ≥ 0 {\displaystyle n\geq r\geq 0} are integers, then
The formal foundation of TDDFT is the Runge–Gross (RG) theorem (1984) [1] – the time-dependent analogue of the Hohenberg–Kohn (HK) theorem (1964). [2] The RG theorem shows that, for a given initial wavefunction, there is a unique mapping between the time-dependent external potential of a system and its time-dependent density.
Format a statement of mathematical theorem or conjecture in box Template parameters [Edit template data] This template prefers block formatting of parameters. Parameter Description Type Status math_statement math_statement 1 Mathematical statement String required name name 2 Name of theorem or Proposition, Conjecture etc Default Theorem Example Claim Line optional note note Note about theorem ...
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Using the standard formalism of probability theory, let and be two random variables defined on probability spaces (,,) and (,,).Then a coupling of and is a new probability space (,,) over which there are two random variables and such that has the same distribution as while has the same distribution as .
Bayesian statistics; Posterior = Likelihood × Prior ÷ Evidence: Background; Bayesian inference; Bayesian probability; Bayes' theorem; Bernstein–von Mises theorem; Coherence; Cox's theorem; Cromwell's rule; Likelihood principle; Principle of indifference; Principle of maximum entropy; Model building; Conjugate prior; Linear regression ...
For example, for a Gaussian distribution with unknown mean and variance, the jointly sufficient statistic, from which maximum likelihood estimates of both parameters can be estimated, consists of two functions, the sum of all data points and the sum of all squared data points (or equivalently, the sample mean and sample variance).