Arg max information

the arguments of the maxima (abbreviated arg max or argmax) and arguments of the minima (abbreviated arg min or argmin) are the input points at which...

considering the arg max as a function with categorical output 1 , … , n {\displaystyle 1,\dots ,n} (corresponding to the index), consider the arg max function...

∈ arg ⁡ min μ G max μ D L ( μ G , μ D ) , μ ^ D ∈ arg ⁡ max μ D L ( μ ^ G , μ D ) , {\displaystyle {\hat {\mu }}_{G}\in \arg \min _{\mu _{G}}\max _{\mu...

1 ) = arg ⁡ max ‖ w ‖ = 1 { ∑ i ( t 1 ) ( i ) 2 } = arg ⁡ max ‖ w ‖ = 1 { ∑ i ( x ( i ) ⋅ w ) 2 } {\displaystyle \mathbf {w} _{(1)}=\arg \max _{\Vert...

then by routine integration, P r ( j = arg ⁡ max i ( g i + log ⁡ π i ) ) = π j ∑ i π i {\displaystyle Pr(j=\arg \max _{i}(g_{i}+\log \pi _{i}))={\frac {\pi...

\theta } is the actual parameter. In maximum likelihood estimation, the arg max (over the parameter θ {\displaystyle \theta } ) of the likelihood function...

use gradient descent to search for arg ⁡ max Z ~ ∑ i log ⁡ P r [ Y i | Z ~ ∗ E ( X i ) ] {\displaystyle \arg \max _{\tilde {Z}}\sum _{i}\log Pr[Y^{i}|{\tilde...

B ⟩ F = arg ⁡ max Ω ⟨ Ω A , B ⟩ F = arg ⁡ max Ω ⟨ Ω , B A T ⟩ F = arg ⁡ max Ω ⟨ Ω , U Σ V T ⟩ F = arg ⁡ max Ω ⟨ U T Ω V , Σ ⟩ F = arg ⁡ max Ω ⟨ S , Σ...

largest diameter: C ∗ = arg ⁡ max C ∈ C max i 1 , i 2 ∈ C δ ( i 1 , i 2 ) {\displaystyle C_{*}=\arg \max _{C\in {\mathcal {C}}}\max _{i_{1},i_{2}\in C}\delta...

that gives the highest score: g ( x ) = arg ⁡ max y f ( x , y ) {\displaystyle g(x)={\underset {y}{\arg \max }}\;f(x,y)} . Let F {\displaystyle F} denote...

{\displaystyle {\hat {\theta }}={\underset {\theta \in \Theta }{\operatorname {arg\;max} }}\,{\mathcal {L}}_{n}(\theta \,;\mathbf {y} )~.} Intuitively, this selects...

is omitted and error is False, the result is abort. if a ≥ b → max := a □ b ≥ a → max := b fi If a = b, either a or b is chosen as the new value for the...

{\displaystyle p} and moving towards the maximum likelihood arg ⁡ max z ln ⁡ p θ ( x | z ) {\displaystyle \arg \max _{z}\ln p_{\theta }(x|z)} . H ( q ϕ ( ⋅ | x ) )...

}(x)&={\underset {\theta }{\operatorname {arg\,max} }}\ f(\theta \mid x)\\&={\underset {\theta }{\operatorname {arg\,max} }}\ {\frac {f(x\mid \theta )\,g(\theta...

correspondence arg ⁡ max x ∈ X f ( x ; s ) {\displaystyle \arg \max _{x\in X}f(x;s)} is said to be increasing if arg ⁡ max x ∈ X f ( x ; s ′ ) ≥ S S O arg ⁡ max x...

estimate arg ⁡ max x p ( x | y ) {\displaystyle \arg \max _{x}p(x|y)} . If we want to force the model to move towards the maximum likelihood estimate arg ⁡ max...

( i ) ) {\displaystyle \theta ^{*}={\underset {\theta }{\operatorname {arg\,max} }}\sum _{i}^{T}P_{\theta }(\mathbf {y} ^{(i)}|\mathbf {x} ^{(i)})} Expanding...

~ = a r g max e ∈ e ∗ p ( e | f ) = a r g max e ∈ e ∗ p ( f | e ) p ( e ) {\displaystyle {\tilde {e}}=arg\max _{e\in e^{*}}p(e|f)=arg\max _{e\in e^{*}}p(f|e)p(e)}...

maximum-likelihood parameter estimate is: α ^ , β ^ = arg ⁡ max α , β 1 n ∑ i = 1 n log ⁡ f ( x i ; α , β ) = arg ⁡ max α , β α ( 1 n ∑ i log ⁡ σ ( x i ) ) + β (...

and {−5, (2k + 1)π}, where k ranges over all integers. Operators arg min and arg max are sometimes also written as argmin and argmax, and stand for argument...

maximized between the two windows: w = arg ⁡ max w ‖ w X 1 ‖ 2 ‖ w X 2 ‖ 2 {\displaystyle \mathbf {w} ={\arg \max }_{\mathbf {w} }{\frac {\left\|\mathbf...

arg ⁡ max x t p ( x t | y 1 : t ) = arg ⁡ max x t α ( x t ) , {\displaystyle {\widehat {x}}_{t}^{MAP}=\arg \max _{x_{t}}\;p(x_{t}|y_{1:t})=\arg \max _{x_{t}}\;\alpha...

function in data analysis Softmax function – Smooth approximation of one-hot arg max Swish function – Mathematical activation function in data analysis Weibull...

\mathbf {I} (A,\mathbf {p} )={\underset {\mathbf {\hat {n}} }{\operatorname {arg\,max} }}\mathbf {\hat {n}} _{\mathbf {p} }{\frac {\mathrm {d} q}{\mathrm {d}...

setting), and it can be defined using the arg max: * ⁡ arg ⁡ max x g ( x ) . {\displaystyle \operatorname {*} {\arg \max }_{\mathbf {x} }g(\mathbf {x} ).} An...

decision function is defined as: f ( x ; w ) = arg ⁡ max y w T ϕ ( x , y ) {\displaystyle f(x;w)=\arg \max _{y}w^{T}\phi (x,y)} According to Memisevic's...