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Husimi Q representation
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quantum effects in superconductors. The Husimi Q distribution (called Q-function in the context of quantum optics) is one of the simplest distributionsExpectation–maximization algorithm (7,615 words) [view diff] exact match in snippet view article find links to article
expressed as equality by using the Q-function of the α-log likelihood ratio and the α-divergence. Obtaining this Q-function is a generalized E step. Its maximizationExtensionality (527 words) [view diff] no match in snippet view article find links to article
extensionality: if P ⟺ Q {\displaystyle P\iff Q} then P = Q {\displaystyle P=Q} Function (functional) extensionality: if ∀ x , f x = g x {\displaystyle \forallQ-learning (3,800 words) [view diff] exact match in snippet view article find links to article
become infinite. Even with a discount factor only slightly lower than 1, Q-function learning leads to propagation of errors and instabilities when the valueWigner quasiprobability distribution (5,299 words) [view diff] no match in snippet view article find links to article
the Segal–Bargmann transform. The reasoning is as follows. The Husimi Q function of ψ {\displaystyle \psi } may be computed as the squared magnitude ofQuantum harmonic oscillator (6,975 words) [view diff] no match in snippet view article find links to article
polynomials are linked through the Wigner map. Meanwhile, the Husimi Q function of the harmonic oscillator eigenstates have an even simpler form. If weSinclair BASIC (5,383 words) [view diff] no match in snippet view article find links to article
are true, else returns false ASN number EXTENDED MODE then SYMBOL SHIFT+Q Function Returns the arcsine of number AT line, column; SYMBOL SHIFT+I Other UsedBirthday problem (7,004 words) [view diff] case mismatch in snippet view article find links to article
Grabner, P. J.; Kirschenhofer, P.; Prodinger, H. (1995). "On Ramanujan's Q-Function". Journal of Computational and Applied Mathematics. 58: 103–116. doi:10Quasi-set theory (1,574 words) [view diff] exact match in snippet view article find links to article
the axiom of separation; an axiom stating the image of a q-set under a q-function is also a q-set; q-set equivalents of the axioms of infinity, regularityDeep reinforcement learning (2,926 words) [view diff] exact match in snippet view article find links to article
discrete action spaces, these algorithms usually learn a neural network Q-function Q ( s , a ) {\displaystyle Q(s,a)} that estimates the future returns takingCompiler Description Language (1,412 words) [view diff] no match in snippet view article find links to article
*again; less+p+m, swap item+p+m, incr+p; less+m+q, swap item+q+m, decr+q; +)). FUNCTION move up + >j + >p> + >m: less+j+p; smaller item+m+p; incr+p, *. FUNCTIONGreen's function for the three-variable Laplace equation (1,910 words) [view diff] exact match in snippet view article find links to article
between the free-space Green's function in cylindrical coordinates and the Q-function expression. Likewise, see another one of Snow's pieces of work, titledReal-root isolation (4,602 words) [view diff] no match in snippet view article find links to article
in the algorithm; if p has integer coefficients, the same is true for q) function bisection is input: p(x), a square-free polynomial, such that p(0) p(1)Bayesian estimation of templates in computational anatomy (4,326 words) [view diff] exact match in snippet view article find links to article
^{\text{old}}}}\|^{2}-\|v_{0}\|_{V}^{2}\end{matrix}}} Compute new template maximizing Q-function, setting θ new ≐ v 0 new = arg max θ = v 0 Q ( θ ; θ old = v 0 old )Bayesian model of computational anatomy (3,931 words) [view diff] exact match in snippet view article find links to article
^{\text{old}}}}\|^{2}-\|v_{0}\|_{V}^{2}\end{cases}}} Compute new template maximizing Q-function, setting θ new ≐ v 0 new = arg max θ = v 0 Q ( θ ; θ old = v 0 old )Construction of a complex null tetrad (3,466 words) [view diff] no match in snippet view article find links to article
isolated horizon. People often Taylor expand a scalar Q {\displaystyle Q} function with respect to the horizon r=0, Q = ∑ i = 0 Q ( i ) r i = Q ( 0 ) +